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Math.NET Numerics: 4.11.0 api update

gh-pages
Christoph Ruegg 6 years ago
parent
commit
e51b84dae5
  1. 2
      api/MathNet.Numerics.Differentiation/FiniteDifferenceCoefficients.htm
  2. 2
      api/MathNet.Numerics.Differentiation/NumericalDerivative.htm
  3. 2
      api/MathNet.Numerics.Differentiation/NumericalHessian.htm
  4. 2
      api/MathNet.Numerics.Differentiation/NumericalJacobian.htm
  5. 2
      api/MathNet.Numerics.Differentiation/StepType.htm
  6. 2
      api/MathNet.Numerics.Differentiation/index.htm
  7. 8
      api/MathNet.Numerics.Distributions/Bernoulli.htm
  8. 8
      api/MathNet.Numerics.Distributions/Beta.htm
  9. 8
      api/MathNet.Numerics.Distributions/BetaScaled.htm
  10. 8
      api/MathNet.Numerics.Distributions/Binomial.htm
  11. 8
      api/MathNet.Numerics.Distributions/Burr.htm
  12. 8
      api/MathNet.Numerics.Distributions/Categorical.htm
  13. 8
      api/MathNet.Numerics.Distributions/Cauchy.htm
  14. 8
      api/MathNet.Numerics.Distributions/Chi.htm
  15. 8
      api/MathNet.Numerics.Distributions/ChiSquared.htm
  16. 8
      api/MathNet.Numerics.Distributions/ContinuousUniform.htm
  17. 8
      api/MathNet.Numerics.Distributions/ConwayMaxwellPoisson.htm
  18. 8
      api/MathNet.Numerics.Distributions/Dirichlet.htm
  19. 8
      api/MathNet.Numerics.Distributions/DiscreteUniform.htm
  20. 8
      api/MathNet.Numerics.Distributions/Erlang.htm
  21. 8
      api/MathNet.Numerics.Distributions/Exponential.htm
  22. 8
      api/MathNet.Numerics.Distributions/FisherSnedecor.htm
  23. 8
      api/MathNet.Numerics.Distributions/Gamma.htm
  24. 8
      api/MathNet.Numerics.Distributions/Geometric.htm
  25. 8
      api/MathNet.Numerics.Distributions/Hypergeometric.htm
  26. 8
      api/MathNet.Numerics.Distributions/IContinuousDistribution.htm
  27. 8
      api/MathNet.Numerics.Distributions/IDiscreteDistribution.htm
  28. 8
      api/MathNet.Numerics.Distributions/IDistribution.htm
  29. 8
      api/MathNet.Numerics.Distributions/IUnivariateDistribution.htm
  30. 8
      api/MathNet.Numerics.Distributions/InverseGamma.htm
  31. 8
      api/MathNet.Numerics.Distributions/InverseGaussian.htm
  32. 8
      api/MathNet.Numerics.Distributions/InverseWishart.htm
  33. 8
      api/MathNet.Numerics.Distributions/Laplace.htm
  34. 8
      api/MathNet.Numerics.Distributions/LogNormal.htm
  35. 8
      api/MathNet.Numerics.Distributions/MatrixNormal.htm
  36. 8
      api/MathNet.Numerics.Distributions/MeanPrecisionPair.htm
  37. 8
      api/MathNet.Numerics.Distributions/Multinomial.htm
  38. 8
      api/MathNet.Numerics.Distributions/NegativeBinomial.htm
  39. 8
      api/MathNet.Numerics.Distributions/Normal.htm
  40. 8
      api/MathNet.Numerics.Distributions/NormalGamma.htm
  41. 8
      api/MathNet.Numerics.Distributions/Pareto.htm
  42. 8
      api/MathNet.Numerics.Distributions/Poisson.htm
  43. 8
      api/MathNet.Numerics.Distributions/Rayleigh.htm
  44. 807
      api/MathNet.Numerics.Distributions/SkewedGeneralizedError.htm
  45. 956
      api/MathNet.Numerics.Distributions/SkewedGeneralizedT.htm
  46. 8
      api/MathNet.Numerics.Distributions/Stable.htm
  47. 8
      api/MathNet.Numerics.Distributions/StudentT.htm
  48. 8
      api/MathNet.Numerics.Distributions/Triangular.htm
  49. 8
      api/MathNet.Numerics.Distributions/TruncatedPareto.htm
  50. 8
      api/MathNet.Numerics.Distributions/Weibull.htm
  51. 8
      api/MathNet.Numerics.Distributions/Wishart.htm
  52. 8
      api/MathNet.Numerics.Distributions/Zipf.htm
  53. 10
      api/MathNet.Numerics.Distributions/index.htm
  54. 2
      api/MathNet.Numerics.Financial/AbsoluteReturnMeasures.htm
  55. 2
      api/MathNet.Numerics.Financial/AbsoluteRiskMeasures.htm
  56. 2
      api/MathNet.Numerics.Financial/index.htm
  57. 2
      api/MathNet.Numerics.IntegralTransforms/Fourier.htm
  58. 2
      api/MathNet.Numerics.IntegralTransforms/FourierOptions.htm
  59. 2
      api/MathNet.Numerics.IntegralTransforms/Hartley.htm
  60. 2
      api/MathNet.Numerics.IntegralTransforms/HartleyOptions.htm
  61. 2
      api/MathNet.Numerics.IntegralTransforms/index.htm
  62. 2
      api/MathNet.Numerics.Integration/DoubleExponentialTransformation.htm
  63. 2
      api/MathNet.Numerics.Integration/GaussKronrodRule.htm
  64. 2
      api/MathNet.Numerics.Integration/GaussLegendreRule.htm
  65. 2
      api/MathNet.Numerics.Integration/NewtonCotesTrapeziumRule.htm
  66. 2
      api/MathNet.Numerics.Integration/SimpsonRule.htm
  67. 2
      api/MathNet.Numerics.Integration/index.htm
  68. 2
      api/MathNet.Numerics.Interpolation/Barycentric.htm
  69. 2
      api/MathNet.Numerics.Interpolation/BulirschStoerRationalInterpolation.htm
  70. 2
      api/MathNet.Numerics.Interpolation/CubicSpline.htm
  71. 2
      api/MathNet.Numerics.Interpolation/IInterpolation.htm
  72. 2
      api/MathNet.Numerics.Interpolation/LinearSpline.htm
  73. 2
      api/MathNet.Numerics.Interpolation/LogLinear.htm
  74. 2
      api/MathNet.Numerics.Interpolation/NevillePolynomialInterpolation.htm
  75. 2
      api/MathNet.Numerics.Interpolation/QuadraticSpline.htm
  76. 2
      api/MathNet.Numerics.Interpolation/SplineBoundaryCondition.htm
  77. 2
      api/MathNet.Numerics.Interpolation/StepInterpolation.htm
  78. 2
      api/MathNet.Numerics.Interpolation/TransformedInterpolation.htm
  79. 2
      api/MathNet.Numerics.Interpolation/index.htm
  80. 2
      api/MathNet.Numerics.LinearAlgebra.Complex.Solvers/BiCgStab.htm
  81. 2
      api/MathNet.Numerics.LinearAlgebra.Complex.Solvers/CompositeSolver.htm
  82. 2
      api/MathNet.Numerics.LinearAlgebra.Complex.Solvers/DiagonalPreconditioner.htm
  83. 2
      api/MathNet.Numerics.LinearAlgebra.Complex.Solvers/GpBiCg.htm
  84. 2
      api/MathNet.Numerics.LinearAlgebra.Complex.Solvers/ILU0Preconditioner.htm
  85. 2
      api/MathNet.Numerics.LinearAlgebra.Complex.Solvers/ILUTPPreconditioner.htm
  86. 2
      api/MathNet.Numerics.LinearAlgebra.Complex.Solvers/MILU0Preconditioner.htm
  87. 2
      api/MathNet.Numerics.LinearAlgebra.Complex.Solvers/MlkBiCgStab.htm
  88. 2
      api/MathNet.Numerics.LinearAlgebra.Complex.Solvers/TFQMR.htm
  89. 2
      api/MathNet.Numerics.LinearAlgebra.Complex.Solvers/index.htm
  90. 2
      api/MathNet.Numerics.LinearAlgebra.Complex/DenseMatrix.htm
  91. 2
      api/MathNet.Numerics.LinearAlgebra.Complex/DenseVector.htm
  92. 2
      api/MathNet.Numerics.LinearAlgebra.Complex/DiagonalMatrix.htm
  93. 2
      api/MathNet.Numerics.LinearAlgebra.Complex/Matrix.htm
  94. 2
      api/MathNet.Numerics.LinearAlgebra.Complex/SparseMatrix.htm
  95. 2
      api/MathNet.Numerics.LinearAlgebra.Complex/SparseVector.htm
  96. 2
      api/MathNet.Numerics.LinearAlgebra.Complex/Vector.htm
  97. 2
      api/MathNet.Numerics.LinearAlgebra.Complex/index.htm
  98. 2
      api/MathNet.Numerics.LinearAlgebra.Complex32.Solvers/BiCgStab.htm
  99. 2
      api/MathNet.Numerics.LinearAlgebra.Complex32.Solvers/CompositeSolver.htm
  100. 2
      api/MathNet.Numerics.LinearAlgebra.Complex32.Solvers/DiagonalPreconditioner.htm

2
api/MathNet.Numerics.Differentiation/FiniteDifferenceCoefficients.htm

@ -286,7 +286,7 @@
</div>
</div>
<div id="footer">
<p>Based on v4.10.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Based on v4.11.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Generated by <a href="http://docu.jagregory.com">docu</a></p>
</div>
</body>

2
api/MathNet.Numerics.Differentiation/NumericalDerivative.htm

@ -540,7 +540,7 @@ h is approximately equal to the square-root of machine accuracy, epsilon.
</div>
</div>
<div id="footer">
<p>Based on v4.10.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Based on v4.11.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Generated by <a href="http://docu.jagregory.com">docu</a></p>
</div>
</body>

2
api/MathNet.Numerics.Differentiation/NumericalHessian.htm

@ -314,7 +314,7 @@ The function mirrors the Hessian along the diagonal since d2f/dxdy = d2f/dydx fo
</div>
</div>
<div id="footer">
<p>Based on v4.10.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Based on v4.11.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Generated by <a href="http://docu.jagregory.com">docu</a></p>
</div>
</body>

2
api/MathNet.Numerics.Differentiation/NumericalJacobian.htm

@ -363,7 +363,7 @@ added efficiency. This method also assumes that the length of vector x consisten
</div>
</div>
<div id="footer">
<p>Based on v4.10.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Based on v4.11.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Generated by <a href="http://docu.jagregory.com">docu</a></p>
</div>
</body>

2
api/MathNet.Numerics.Differentiation/StepType.htm

@ -338,7 +338,7 @@ input parameter. Although implementation may vary, an example of second order ac
</div>
</div>
<div id="footer">
<p>Based on v4.10.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Based on v4.11.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Generated by <a href="http://docu.jagregory.com">docu</a></p>
</div>
</body>

2
api/MathNet.Numerics.Differentiation/index.htm

@ -159,7 +159,7 @@
</div>
<div id="footer">
<p>Based on v4.10.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Based on v4.11.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Generated by <a href="http://docu.jagregory.com">docu</a></p>
</div>
</body>

8
api/MathNet.Numerics.Distributions/Bernoulli.htm

@ -237,6 +237,12 @@
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Rayleigh.htm">Rayleigh</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm">SkewedGeneralizedError</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm">SkewedGeneralizedT</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Stable.htm">Stable</a>
@ -791,7 +797,7 @@ p specifies the probability that a 1 is generated..
</div>
</div>
<div id="footer">
<p>Based on v4.10.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Based on v4.11.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Generated by <a href="http://docu.jagregory.com">docu</a></p>
</div>
</body>

8
api/MathNet.Numerics.Distributions/Beta.htm

@ -237,6 +237,12 @@
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Rayleigh.htm">Rayleigh</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm">SkewedGeneralizedError</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm">SkewedGeneralizedT</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Stable.htm">Stable</a>
@ -873,7 +879,7 @@ at the given probability. This is also known as the quantile or percent point fu
</div>
</div>
<div id="footer">
<p>Based on v4.10.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Based on v4.11.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Generated by <a href="http://docu.jagregory.com">docu</a></p>
</div>
</body>

8
api/MathNet.Numerics.Distributions/BetaScaled.htm

@ -237,6 +237,12 @@
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Rayleigh.htm">Rayleigh</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm">SkewedGeneralizedError</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm">SkewedGeneralizedT</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Stable.htm">Stable</a>
@ -957,7 +963,7 @@ at the given probability. This is also known as the quantile or percent point fu
</div>
</div>
<div id="footer">
<p>Based on v4.10.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Based on v4.11.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Generated by <a href="http://docu.jagregory.com">docu</a></p>
</div>
</body>

8
api/MathNet.Numerics.Distributions/Binomial.htm

@ -237,6 +237,12 @@
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Rayleigh.htm">Rayleigh</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm">SkewedGeneralizedError</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm">SkewedGeneralizedT</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Stable.htm">Stable</a>
@ -823,7 +829,7 @@ For details about this distribution, see. <blockquote class="remarks">
</div>
</div>
<div id="footer">
<p>Based on v4.10.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Based on v4.11.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Generated by <a href="http://docu.jagregory.com">docu</a></p>
</div>
</body>

8
api/MathNet.Numerics.Distributions/Burr.htm

@ -237,6 +237,12 @@
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Rayleigh.htm">Rayleigh</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm">SkewedGeneralizedError</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm">SkewedGeneralizedT</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Stable.htm">Stable</a>
@ -767,7 +773,7 @@
</div>
</div>
<div id="footer">
<p>Based on v4.10.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Based on v4.11.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Generated by <a href="http://docu.jagregory.com">docu</a></p>
</div>
</body>

8
api/MathNet.Numerics.Distributions/Categorical.htm

@ -237,6 +237,12 @@
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Rayleigh.htm">Rayleigh</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm">SkewedGeneralizedError</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm">SkewedGeneralizedT</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Stable.htm">Stable</a>
@ -1050,7 +1056,7 @@ at the given probability.
</div>
</div>
<div id="footer">
<p>Based on v4.10.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Based on v4.11.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Generated by <a href="http://docu.jagregory.com">docu</a></p>
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</body>

8
api/MathNet.Numerics.Distributions/Cauchy.htm

@ -237,6 +237,12 @@
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Rayleigh.htm">Rayleigh</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm">SkewedGeneralizedError</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm">SkewedGeneralizedT</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Stable.htm">Stable</a>
@ -872,7 +878,7 @@ at the given probability. This is also known as the quantile or percent point fu
</div>
</div>
<div id="footer">
<p>Based on v4.10.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Based on v4.11.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Generated by <a href="http://docu.jagregory.com">docu</a></p>
</div>
</body>

8
api/MathNet.Numerics.Distributions/Chi.htm

@ -237,6 +237,12 @@
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Rayleigh.htm">Rayleigh</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm">SkewedGeneralizedError</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm">SkewedGeneralizedT</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Stable.htm">Stable</a>
@ -785,7 +791,7 @@ then have a chi distribution..
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<div id="footer">
<p>Based on v4.10.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Based on v4.11.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Generated by <a href="http://docu.jagregory.com">docu</a></p>
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</body>

8
api/MathNet.Numerics.Distributions/ChiSquared.htm

@ -237,6 +237,12 @@
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Rayleigh.htm">Rayleigh</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm">SkewedGeneralizedError</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm">SkewedGeneralizedT</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Stable.htm">Stable</a>
@ -829,7 +835,7 @@ at the given probability. This is also known as the quantile or percent point fu
</div>
</div>
<div id="footer">
<p>Based on v4.10.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Based on v4.11.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Generated by <a href="http://docu.jagregory.com">docu</a></p>
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</body>

8
api/MathNet.Numerics.Distributions/ContinuousUniform.htm

@ -237,6 +237,12 @@
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Rayleigh.htm">Rayleigh</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm">SkewedGeneralizedError</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm">SkewedGeneralizedT</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Stable.htm">Stable</a>
@ -872,7 +878,7 @@ at the given probability. This is also known as the quantile or percent point fu
</div>
</div>
<div id="footer">
<p>Based on v4.10.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Based on v4.11.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Generated by <a href="http://docu.jagregory.com">docu</a></p>
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8
api/MathNet.Numerics.Distributions/ConwayMaxwellPoisson.htm

@ -237,6 +237,12 @@
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Rayleigh.htm">Rayleigh</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm">SkewedGeneralizedError</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm">SkewedGeneralizedT</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Stable.htm">Stable</a>
@ -784,7 +790,7 @@ distributions. It is parameterized by two real numbers "lambda" and "nu". For <d
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<p>Based on v4.10.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Based on v4.11.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Generated by <a href="http://docu.jagregory.com">docu</a></p>
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8
api/MathNet.Numerics.Distributions/Dirichlet.htm

@ -237,6 +237,12 @@
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Rayleigh.htm">Rayleigh</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm">SkewedGeneralizedError</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm">SkewedGeneralizedT</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Stable.htm">Stable</a>
@ -563,7 +569,7 @@ You can also leave out the last <var>x</var> component, and it will be compute
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<p>Based on v4.10.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
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8
api/MathNet.Numerics.Distributions/DiscreteUniform.htm

@ -237,6 +237,12 @@
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Rayleigh.htm">Rayleigh</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm">SkewedGeneralizedError</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm">SkewedGeneralizedT</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Stable.htm">Stable</a>
@ -815,7 +821,7 @@ is parameterized by a lower and upper bound (both inclusive)..
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8
api/MathNet.Numerics.Distributions/Erlang.htm

@ -237,6 +237,12 @@
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@ -800,7 +806,7 @@ No closed form analytical expression exists, so this value is approximated numer
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@ -520,7 +526,7 @@ a Wishart random variable and inverting the matrix.
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@ -370,7 +376,7 @@ is defined.
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@ -601,7 +607,7 @@ as this is often impossible using floating point arithmetic. </p>
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@ -786,7 +792,7 @@ when the probability of success is p..
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@ -993,7 +999,7 @@ at the given probability. This is also known as the quantile or percent point fu
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@ -678,7 +684,7 @@ will be positive infinity. A completely degenerate NormalGamma distribution with
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@ -864,7 +870,7 @@ at the given probability. This is also known as the quantile or percent point fu
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@ -832,7 +838,7 @@ at the given probability. This is also known as the quantile or percent point fu
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<div class="header">
<p class="class"><strong>Type</strong> SkewedGeneralizedError</p>
<p><strong>Namespace</strong> MathNet.Numerics.Distributions</p>
<p><strong>Interfaces</strong> <a href="../MathNet.Numerics.Distributions/IContinuousDistribution.htm">IContinuousDistribution</a></p>
</div>
<div class="sub-header">
<div id="summary">Continuous Univariate Skewed Generalized Error Distribution (SGED).
Implements the univariate SSkewed Generalized Error Distribution. For details about this
distribution, see.
It includes Laplace, Normal and Student-t distributions.
This is the <a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm">SkewedGeneralizedT</a> distribution with q=Inf. <blockquote class="remarks">
<p>This implementation is based on the R package dsgt and corresponding viginette, see. Compared to that
implementation, the options for mean adjustment and variance adjustment are always true.
The location (μ) is the mean of the distribution.
The scale (σ) squared is the variance of the distribution. </p> <p>The distribution will use the <a href="../System/Random.htm">Random</a> by
default. Users can get/set the random number generator by using the <a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#RandomSource">RandomSource</a> property. </p> <p>The statistics classes will check all the incoming parameters
whether they are in the allowed range. </p>
</blockquote>
</div>
<h3 class="section">Constructors</h3>
<ul>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#.ctor">SkewedGeneralizedError</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#.ctor">SkewedGeneralizedError</a></li>
</ul>
<h3 class="section">Static Functions</h3>
<ul>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#CDF">CDF</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#InvCDF">InvCDF</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#IsValidParameterSet">IsValidParameterSet</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#PDF">PDF</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#PDFLn">PDFLn</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#Sample">Sample</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#Sample">Sample</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#Samples">Samples</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#Samples">Samples</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#Samples">Samples</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#Samples">Samples</a></li>
</ul>
<h3 class="section">Methods</h3>
<ul>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#CumulativeDistribution">CumulativeDistribution</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#Density">Density</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#DensityLn">DensityLn</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#Equals">Equals</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#GetHashCode">GetHashCode</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#GetType">GetType</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#InverseCumulativeDistribution">InverseCumulativeDistribution</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#Sample">Sample</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#Samples">Samples</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#Samples">Samples</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#ToString">ToString</a></li>
</ul>
<h3 class="section">Properties</h3>
<ul>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#Entropy">Entropy</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#Location">Location</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#Maximum">Maximum</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#Mean">Mean</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#Median">Median</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#Minimum">Minimum</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#Mode">Mode</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#P">P</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#RandomSource">RandomSource</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#Scale">Scale</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#Skew">Skew</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#Skewness">Skewness</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#StdDev">StdDev</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm#Variance">Variance</a></li>
</ul>
</div>
<h3 class="section">Public Constructors</h3>
<div id=".ctor" class="method">
<h4> <strong>SkewedGeneralizedError</strong>(<span title="System.double">double</span> location, <span title="System.double">double</span> scale, <span title="System.double">double</span> skew, <span title="System.double">double</span> p)</h4>
<div class="content">Initializes a new instance of the SkewedGeneralizedT class with a particular location, scale, skew
and kurtosis parameters. Different parameterizations result in different distributions.
<div class="parameters">
<h5>Parameters</h5>
<h6><code><span title="System.double">double</span></code> location</h6>
<p class="comments">The location (μ) of the distribution. </p>
<h6><code><span title="System.double">double</span></code> scale</h6>
<p class="comments">The scale (σ) of the distribution. Range: σ > 0. </p>
<h6><code><span title="System.double">double</span></code> skew</h6>
<p class="comments">The skew, 1 > λ > -1 </p>
<h6><code><span title="System.double">double</span></code> p</h6>
<p class="comments">Parameter that controls kurtosis. Range: p > 0 </p>
</div>
</div>
</div>
<div id=".ctor" class="method">
<h4> <strong>SkewedGeneralizedError</strong>()</h4>
<div class="content">Initializes a new instance of the SkewedGeneralizedError class. This is a generalized error distribution
with location=0.0, scale=1.0, skew=0.0 and p=2.0 (a standard normal distribution).
</div>
</div>
<h3 class="section">Public Static Functions</h3>
<div id="CDF" class="method">
<h4><span title="System.double">double</span> <strong>CDF</strong>(<span title="System.double">double</span> location, <span title="System.double">double</span> scale, <span title="System.double">double</span> skew, <span title="System.double">double</span> p, <span title="System.double">double</span> x)</h4>
<div class="content">
</div>
</div>
<div id="InvCDF" class="method">
<h4><span title="System.double">double</span> <strong>InvCDF</strong>(<span title="System.double">double</span> location, <span title="System.double">double</span> scale, <span title="System.double">double</span> skew, <span title="System.double">double</span> p, <span title="System.double">double</span> pr)</h4>
<div class="content">
</div>
</div>
<div id="IsValidParameterSet" class="method">
<h4><span title="System.bool">bool</span> <strong>IsValidParameterSet</strong>(<span title="System.double">double</span> location, <span title="System.double">double</span> scale, <span title="System.double">double</span> skew, <span title="System.double">double</span> p)</h4>
<div class="content">Tests whether the provided values are valid parameters for this distribution.
<div class="parameters">
<h5>Parameters</h5>
<h6><code><span title="System.double">double</span></code> location</h6>
<p class="comments">The location (μ) of the distribution. </p>
<h6><code><span title="System.double">double</span></code> scale</h6>
<p class="comments">The scale (σ) of the distribution. Range: σ > 0. </p>
<h6><code><span title="System.double">double</span></code> skew</h6>
<p class="comments">The skew, 1 > λ > -1 </p>
<h6><code><span title="System.double">double</span></code> p</h6>
<p class="comments">Parameter that controls kurtosis. Range: p > 0 </p>
</div>
</div>
</div>
<div id="PDF" class="method">
<h4><span title="System.double">double</span> <strong>PDF</strong>(<span title="System.double">double</span> location, <span title="System.double">double</span> scale, <span title="System.double">double</span> skew, <span title="System.double">double</span> p, <span title="System.double">double</span> x)</h4>
<div class="content">
</div>
</div>
<div id="PDFLn" class="method">
<h4><span title="System.double">double</span> <strong>PDFLn</strong>(<span title="System.double">double</span> location, <span title="System.double">double</span> scale, <span title="System.double">double</span> skew, <span title="System.double">double</span> p, <span title="System.double">double</span> x)</h4>
<div class="content">
</div>
</div>
<div id="Sample" class="method">
<h4><span title="System.double">double</span> <strong>Sample</strong>(<span title="System.double">double</span> location, <span title="System.double">double</span> scale, <span title="System.double">double</span> skew, <span title="System.double">double</span> p)</h4>
<div class="content">Generates a sample from the Skew Generalized Error distribution.
<div class="parameters">
<h5>Parameters</h5>
<h6><code><span title="System.double">double</span></code> location</h6>
<p class="comments">The location (μ) of the distribution. </p>
<h6><code><span title="System.double">double</span></code> scale</h6>
<p class="comments">The scale (σ) of the distribution. Range: σ > 0. </p>
<h6><code><span title="System.double">double</span></code> skew</h6>
<p class="comments">The skew, 1 > λ > -1 </p>
<h6><code><span title="System.double">double</span></code> p</h6>
<p class="comments">Parameter that controls kurtosis. Range: p > 0 </p>
</div>
<div class="return">
<h5>Return</h5>
<h6><code><span title="System.double">double</span></code></h6>
<p>a sample from the distribution. </p>
</div>
</div>
</div>
<div id="Sample" class="method">
<h4><span title="System.double">double</span> <strong>Sample</strong>(<span title="System.Random">Random</span> rnd, <span title="System.double">double</span> location, <span title="System.double">double</span> scale, <span title="System.double">double</span> skew, <span title="System.double">double</span> p)</h4>
<div class="content">Generates a sample from the Skew Generalized Error distribution.
<div class="parameters">
<h5>Parameters</h5>
<h6><code><span title="System.Random">Random</span></code> rnd</h6>
<p class="comments">The random number generator to use. </p>
<h6><code><span title="System.double">double</span></code> location</h6>
<p class="comments">The location (μ) of the distribution. </p>
<h6><code><span title="System.double">double</span></code> scale</h6>
<p class="comments">The scale (σ) of the distribution. Range: σ > 0. </p>
<h6><code><span title="System.double">double</span></code> skew</h6>
<p class="comments">The skew, 1 > λ > -1 </p>
<h6><code><span title="System.double">double</span></code> p</h6>
<p class="comments">Parameter that controls kurtosis. Range: p > 0 </p>
</div>
<div class="return">
<h5>Return</h5>
<h6><code><span title="System.double">double</span></code></h6>
<p>a sample from the distribution. </p>
</div>
</div>
</div>
<div id="Samples" class="method">
<h4><span title="System.void">void</span> <strong>Samples</strong>(<span title="System.Double[]">Double[]</span> values, <span title="System.double">double</span> location, <span title="System.double">double</span> scale, <span title="System.double">double</span> skew, <span title="System.double">double</span> p)</h4>
<div class="content">Fills an array with samples from the Skew Generalized Error distribution using inverse transform.
<div class="parameters">
<h5>Parameters</h5>
<h6><code><span title="System.Double[]">Double[]</span></code> values</h6>
<p class="comments">The array to fill with the samples. </p>
<h6><code><span title="System.double">double</span></code> location</h6>
<p class="comments">The location (μ) of the distribution. </p>
<h6><code><span title="System.double">double</span></code> scale</h6>
<p class="comments">The scale (σ) of the distribution. Range: σ > 0. </p>
<h6><code><span title="System.double">double</span></code> skew</h6>
<p class="comments">The skew, 1 > λ > -1 </p>
<h6><code><span title="System.double">double</span></code> p</h6>
<p class="comments">Parameter that controls kurtosis. Range: p > 0 </p>
</div>
<div class="return">
<h5>Return</h5>
<h6><code><span title="System.void">void</span></code></h6>
<p>a sequence of samples from the distribution. </p>
</div>
</div>
</div>
<div id="Samples" class="method">
<h4><span title="System.void">void</span> <strong>Samples</strong>(<span title="System.Random">Random</span> rnd, <span title="System.Double[]">Double[]</span> values, <span title="System.double">double</span> location, <span title="System.double">double</span> scale, <span title="System.double">double</span> skew, <span title="System.double">double</span> p)</h4>
<div class="content">Fills an array with samples from the Skew Generalized Error distribution using inverse transform.
<div class="parameters">
<h5>Parameters</h5>
<h6><code><span title="System.Random">Random</span></code> rnd</h6>
<p class="comments">The random number generator to use. </p>
<h6><code><span title="System.Double[]">Double[]</span></code> values</h6>
<p class="comments">The array to fill with the samples. </p>
<h6><code><span title="System.double">double</span></code> location</h6>
<p class="comments">The location (μ) of the distribution. </p>
<h6><code><span title="System.double">double</span></code> scale</h6>
<p class="comments">The scale (σ) of the distribution. Range: σ > 0. </p>
<h6><code><span title="System.double">double</span></code> skew</h6>
<p class="comments">The skew, 1 > λ > -1 </p>
<h6><code><span title="System.double">double</span></code> p</h6>
<p class="comments">Parameter that controls kurtosis. Range: p > 0 </p>
</div>
<div class="return">
<h5>Return</h5>
<h6><code><span title="System.void">void</span></code></h6>
<p>a sequence of samples from the distribution. </p>
</div>
</div>
</div>
<div id="Samples" class="method">
<h4><span title="System.Collections.Generic.IEnumerable<double>">IEnumerable&lt;double&gt;</span> <strong>Samples</strong>(<span title="System.Random">Random</span> rnd, <span title="System.double">double</span> location, <span title="System.double">double</span> scale, <span title="System.double">double</span> skew, <span title="System.double">double</span> p)</h4>
<div class="content">Generates a sequence of samples from the Skew Generalized Error distribution using inverse transform.
<div class="parameters">
<h5>Parameters</h5>
<h6><code><span title="System.Random">Random</span></code> rnd</h6>
<p class="comments">The random number generator to use. </p>
<h6><code><span title="System.double">double</span></code> location</h6>
<p class="comments">The location (μ) of the distribution. </p>
<h6><code><span title="System.double">double</span></code> scale</h6>
<p class="comments">The scale (σ) of the distribution. Range: σ > 0. </p>
<h6><code><span title="System.double">double</span></code> skew</h6>
<p class="comments">The skew, 1 > λ > -1 </p>
<h6><code><span title="System.double">double</span></code> p</h6>
<p class="comments">Parameter that controls kurtosis. Range: p > 0 </p>
</div>
<div class="return">
<h5>Return</h5>
<h6><code><span title="System.Collections.Generic.IEnumerable<double>">IEnumerable&lt;double&gt;</span></code></h6>
<p>a sequence of samples from the distribution. </p>
</div>
</div>
</div>
<div id="Samples" class="method">
<h4><span title="System.Collections.Generic.IEnumerable<double>">IEnumerable&lt;double&gt;</span> <strong>Samples</strong>(<span title="System.double">double</span> location, <span title="System.double">double</span> scale, <span title="System.double">double</span> skew, <span title="System.double">double</span> p)</h4>
<div class="content">Generates a sequence of samples from the Skew Generalized Error distribution using inverse transform.
<div class="parameters">
<h5>Parameters</h5>
<h6><code><span title="System.double">double</span></code> location</h6>
<p class="comments">The location (μ) of the distribution. </p>
<h6><code><span title="System.double">double</span></code> scale</h6>
<p class="comments">The scale (σ) of the distribution. Range: σ > 0. </p>
<h6><code><span title="System.double">double</span></code> skew</h6>
<p class="comments">The skew, 1 > λ > -1 </p>
<h6><code><span title="System.double">double</span></code> p</h6>
<p class="comments">Parameter that controls kurtosis. Range: p > 0 </p>
</div>
<div class="return">
<h5>Return</h5>
<h6><code><span title="System.Collections.Generic.IEnumerable<double>">IEnumerable&lt;double&gt;</span></code></h6>
<p>a sequence of samples from the distribution. </p>
</div>
</div>
</div>
<h3 class="section">Public Methods</h3>
<div id="CumulativeDistribution" class="method">
<h4><span title="System.double">double</span> <strong>CumulativeDistribution</strong>(<span title="System.double">double</span> x)</h4>
<div class="content">
</div>
</div>
<div id="Density" class="method">
<h4><span title="System.double">double</span> <strong>Density</strong>(<span title="System.double">double</span> x)</h4>
<div class="content">
</div>
</div>
<div id="DensityLn" class="method">
<h4><span title="System.double">double</span> <strong>DensityLn</strong>(<span title="System.double">double</span> x)</h4>
<div class="content">
</div>
</div>
<div id="Equals" class="method">
<h4><span title="System.bool">bool</span> <strong>Equals</strong>(<span title="System.object">object</span> obj)</h4>
<div class="content">
</div>
</div>
<div id="GetHashCode" class="method">
<h4><span title="System.int">int</span> <strong>GetHashCode</strong>()</h4>
<div class="content">
</div>
</div>
<div id="GetType" class="method">
<h4><span title="System.Type">Type</span> <strong>GetType</strong>()</h4>
<div class="content">
</div>
</div>
<div id="InverseCumulativeDistribution" class="method">
<h4><span title="System.double">double</span> <strong>InverseCumulativeDistribution</strong>(<span title="System.double">double</span> p)</h4>
<div class="content">
</div>
</div>
<div id="Sample" class="method">
<h4><span title="System.double">double</span> <strong>Sample</strong>()</h4>
<div class="content">
</div>
</div>
<div id="Samples" class="method">
<h4><span title="System.void">void</span> <strong>Samples</strong>(<span title="System.Double[]">Double[]</span> values)</h4>
<div class="content">
</div>
</div>
<div id="Samples" class="method">
<h4><span title="System.Collections.Generic.IEnumerable<double>">IEnumerable&lt;double&gt;</span> <strong>Samples</strong>()</h4>
<div class="content">
</div>
</div>
<div id="ToString" class="method">
<h4><span title="System.string">string</span> <strong>ToString</strong>()</h4>
<div class="content">A string representation of the distribution.
<div class="return">
<h5>Return</h5>
<h6><code><span title="System.string">string</span></code></h6>
<p>a string representation of the distribution. </p>
</div>
</div>
</div>
<h3 class="section">Public Properties</h3>
<div id="Entropy" class="method">
<h4><span title="System.double">double</span> <strong>Entropy</strong> get; </h4>
<div class="content">
</div>
</div>
<div id="Location" class="method">
<h4><span title="System.double">double</span> <strong>Location</strong> get; set;</h4>
<div class="content">Gets the location (μ) of the Skewed Generalized t-distribution.
</div>
</div>
<div id="Maximum" class="method">
<h4><span title="System.double">double</span> <strong>Maximum</strong> get; </h4>
<div class="content">
</div>
</div>
<div id="Mean" class="method">
<h4><span title="System.double">double</span> <strong>Mean</strong> get; </h4>
<div class="content">
</div>
</div>
<div id="Median" class="method">
<h4><span title="System.double">double</span> <strong>Median</strong> get; </h4>
<div class="content">
</div>
</div>
<div id="Minimum" class="method">
<h4><span title="System.double">double</span> <strong>Minimum</strong> get; </h4>
<div class="content">
</div>
</div>
<div id="Mode" class="method">
<h4><span title="System.double">double</span> <strong>Mode</strong> get; </h4>
<div class="content">
</div>
</div>
<div id="P" class="method">
<h4><span title="System.double">double</span> <strong>P</strong> get; set;</h4>
<div class="content">Gets the parameter that controls the kurtosis of the distribution. Range: p > 0.
</div>
</div>
<div id="RandomSource" class="method">
<h4><span title="System.Random">Random</span> <strong>RandomSource</strong> get; set;</h4>
<div class="content">Gets or sets the random number generator which is used to draw random samples.
</div>
</div>
<div id="Scale" class="method">
<h4><span title="System.double">double</span> <strong>Scale</strong> get; set;</h4>
<div class="content">Gets the scale (σ) of the Skewed Generalized t-distribution. Range: σ > 0.
</div>
</div>
<div id="Skew" class="method">
<h4><span title="System.double">double</span> <strong>Skew</strong> get; set;</h4>
<div class="content">Gets the skew (λ) of the Skewed Generalized t-distribution. Range: 1 > λ > -1.
</div>
</div>
<div id="Skewness" class="method">
<h4><span title="System.double">double</span> <strong>Skewness</strong> get; </h4>
<div class="content">
</div>
</div>
<div id="StdDev" class="method">
<h4><span title="System.double">double</span> <strong>StdDev</strong> get; </h4>
<div class="content">
</div>
</div>
<div id="Variance" class="method">
<h4><span title="System.double">double</span> <strong>Variance</strong> get; </h4>
<div class="content">
</div>
</div>
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<p>Based on v4.11.0.0 of MathNet.Numerics (Math.NET Numerics)</p>
<p>Generated by <a href="http://docu.jagregory.com">docu</a></p>
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<div class="header">
<p class="class"><strong>Type</strong> SkewedGeneralizedT</p>
<p><strong>Namespace</strong> MathNet.Numerics.Distributions</p>
<p><strong>Interfaces</strong> <a href="../MathNet.Numerics.Distributions/IContinuousDistribution.htm">IContinuousDistribution</a></p>
</div>
<div class="sub-header">
<div id="summary">Continuous Univariate Skewed Generalized T-distribution.
Implements the univariate Skewed Generalized t-distribution. For details about this
distribution, see.
The skewed generalized t-distribution contains many different distributions within it
as special cases based on the parameterization chosen. <blockquote class="remarks">
<p>This implementation is based on the R package dsgt and corresponding viginette, see. Compared to that
implementation, the options for mean adjustment and variance adjustment are always true.
The location (μ) is the mean of the distribution.
The scale (σ) squared is the variance of the distribution. </p> <p>The distribution will use the <a href="../System/Random.htm">Random</a> by
default. Users can get/set the random number generator by using the <a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#RandomSource">RandomSource</a> property. </p> <p>The statistics classes will check all the incoming parameters
whether they are in the allowed range. </p>
</blockquote>
</div>
<h3 class="section">Constructors</h3>
<ul>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#.ctor">SkewedGeneralizedT</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#.ctor">SkewedGeneralizedT</a></li>
</ul>
<h3 class="section">Static Functions</h3>
<ul>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#CDF">CDF</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#FindSpecializedDistribution">FindSpecializedDistribution</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#InvCDF">InvCDF</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#IsValidParameterSet">IsValidParameterSet</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#PDF">PDF</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#PDFLn">PDFLn</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#Sample">Sample</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#Sample">Sample</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#Samples">Samples</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#Samples">Samples</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#Samples">Samples</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#Samples">Samples</a></li>
</ul>
<h3 class="section">Methods</h3>
<ul>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#CumulativeDistribution">CumulativeDistribution</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#Density">Density</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#DensityLn">DensityLn</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#Equals">Equals</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#GetHashCode">GetHashCode</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#GetType">GetType</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#InverseCumulativeDistribution">InverseCumulativeDistribution</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#Sample">Sample</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#Samples">Samples</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#Samples">Samples</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#ToString">ToString</a></li>
</ul>
<h3 class="section">Properties</h3>
<ul>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#Entropy">Entropy</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#Location">Location</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#Maximum">Maximum</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#Mean">Mean</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#Median">Median</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#Minimum">Minimum</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#Mode">Mode</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#P">P</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#Q">Q</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#RandomSource">RandomSource</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#Scale">Scale</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#Skew">Skew</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#Skewness">Skewness</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#StdDev">StdDev</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm#Variance">Variance</a></li>
</ul>
</div>
<h3 class="section">Public Constructors</h3>
<div id=".ctor" class="method">
<h4> <strong>SkewedGeneralizedT</strong>(<span title="System.double">double</span> location, <span title="System.double">double</span> scale, <span title="System.double">double</span> skew, <span title="System.double">double</span> p, <span title="System.double">double</span> q)</h4>
<div class="content">Initializes a new instance of the SkewedGeneralizedT class with a particular location, scale, skew
and kurtosis parameters. Different parameterizations result in different distributions.
<div class="parameters">
<h5>Parameters</h5>
<h6><code><span title="System.double">double</span></code> location</h6>
<p class="comments">The location (μ) of the distribution. </p>
<h6><code><span title="System.double">double</span></code> scale</h6>
<p class="comments">The scale (σ) of the distribution. Range: σ > 0. </p>
<h6><code><span title="System.double">double</span></code> skew</h6>
<p class="comments">The skew, 1 > λ > -1 </p>
<h6><code><span title="System.double">double</span></code> p</h6>
<p class="comments">First parameter that controls kurtosis. Range: p > 0 </p>
<h6><code><span title="System.double">double</span></code> q</h6>
<p class="comments">Second parameter that controls kurtosis. Range: q > 0 </p>
</div>
</div>
</div>
<div id=".ctor" class="method">
<h4> <strong>SkewedGeneralizedT</strong>()</h4>
<div class="content">Initializes a new instance of the SkewedGeneralizedT class. This is a skewed generalized t-distribution
with location=0.0, scale=1.0, skew=0.0, p=2.0 and q=Inf (a standard normal distribution).
</div>
</div>
<h3 class="section">Public Static Functions</h3>
<div id="CDF" class="method">
<h4><span title="System.double">double</span> <strong>CDF</strong>(<span title="System.double">double</span> location, <span title="System.double">double</span> scale, <span title="System.double">double</span> skew, <span title="System.double">double</span> p, <span title="System.double">double</span> q, <span title="System.double">double</span> x)</h4>
<div class="content">Computes the cumulative distribution (CDF) of the distribution at x, i.e. P(X ≤ x).
<div class="parameters">
<h5>Parameters</h5>
<h6><code><span title="System.double">double</span></code> location</h6>
<p class="comments">The location (μ) of the distribution. </p>
<h6><code><span title="System.double">double</span></code> scale</h6>
<p class="comments">The scale (σ) of the distribution. Range: σ > 0. </p>
<h6><code><span title="System.double">double</span></code> skew</h6>
<p class="comments">The skew, 1 > λ > -1 </p>
<h6><code><span title="System.double">double</span></code> p</h6>
<p class="comments">First parameter that controls kurtosis. Range: p > 0 </p>
<h6><code><span title="System.double">double</span></code> q</h6>
<p class="comments">Second parameter that controls kurtosis. Range: q > 0 </p>
<h6><code><span title="System.double">double</span></code> x</h6>
<p class="comments">The location at which to compute the cumulative distribution function. </p>
</div>
<div class="return">
<h5>Return</h5>
<h6><code><span title="System.double">double</span></code></h6>
<p>the cumulative distribution at location <var>x</var>. </p>
</div>
</div>
</div>
<div id="FindSpecializedDistribution" class="method">
<h4><a href="../MathNet.Numerics.Distributions/IContinuousDistribution.htm">IContinuousDistribution</a> <strong>FindSpecializedDistribution</strong>(<span title="System.double">double</span> location, <span title="System.double">double</span> scale, <span title="System.double">double</span> skew, <span title="System.double">double</span> p, <span title="System.double">double</span> q)</h4>
<div class="content">Given a parameter set, returns the distribution that matches this parameterization.
<div class="parameters">
<h5>Parameters</h5>
<h6><code><span title="System.double">double</span></code> location</h6>
<p class="comments">The location (μ) of the distribution. </p>
<h6><code><span title="System.double">double</span></code> scale</h6>
<p class="comments">The scale (σ) of the distribution. Range: σ > 0. </p>
<h6><code><span title="System.double">double</span></code> skew</h6>
<p class="comments">The skew, 1 > λ > -1 </p>
<h6><code><span title="System.double">double</span></code> p</h6>
<p class="comments">First parameter that controls kurtosis. Range: p > 0 </p>
<h6><code><span title="System.double">double</span></code> q</h6>
<p class="comments">Second parameter that controls kurtosis. Range: q > 0 </p>
</div>
<div class="return">
<h5>Return</h5>
<h6><code><a href="../MathNet.Numerics.Distributions/IContinuousDistribution.htm">IContinuousDistribution</a></code></h6>
<p>Null if no known distribution matches the parameterization, else the distribution. </p>
</div>
</div>
</div>
<div id="InvCDF" class="method">
<h4><span title="System.double">double</span> <strong>InvCDF</strong>(<span title="System.double">double</span> location, <span title="System.double">double</span> scale, <span title="System.double">double</span> skew, <span title="System.double">double</span> p, <span title="System.double">double</span> q, <span title="System.double">double</span> pr)</h4>
<div class="content">Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
at the given probability. This is also known as the quantile or percent point function.
<div class="parameters">
<h5>Parameters</h5>
<h6><code><span title="System.double">double</span></code> location</h6>
<p class="comments">The location (μ) of the distribution. </p>
<h6><code><span title="System.double">double</span></code> scale</h6>
<p class="comments">The scale (σ) of the distribution. Range: σ > 0. </p>
<h6><code><span title="System.double">double</span></code> skew</h6>
<p class="comments">The skew, 1 > λ > -1 </p>
<h6><code><span title="System.double">double</span></code> p</h6>
<p class="comments">First parameter that controls kurtosis. Range: p > 0 </p>
<h6><code><span title="System.double">double</span></code> q</h6>
<p class="comments">Second parameter that controls kurtosis. Range: q > 0 </p>
<h6><code><span title="System.double">double</span></code> pr</h6>
<p class="comments">The location at which to compute the inverse cumulative density. </p>
</div>
<div class="return">
<h5>Return</h5>
<h6><code><span title="System.double">double</span></code></h6>
<p>the inverse cumulative density at <var>p</var>. </p>
</div>
</div>
</div>
<div id="IsValidParameterSet" class="method">
<h4><span title="System.bool">bool</span> <strong>IsValidParameterSet</strong>(<span title="System.double">double</span> location, <span title="System.double">double</span> scale, <span title="System.double">double</span> skew, <span title="System.double">double</span> p, <span title="System.double">double</span> q)</h4>
<div class="content">Tests whether the provided values are valid parameters for this distribution.
<div class="parameters">
<h5>Parameters</h5>
<h6><code><span title="System.double">double</span></code> location</h6>
<p class="comments">The location (μ) of the distribution. </p>
<h6><code><span title="System.double">double</span></code> scale</h6>
<p class="comments">The scale (σ) of the distribution. Range: σ > 0. </p>
<h6><code><span title="System.double">double</span></code> skew</h6>
<p class="comments">The skew, 1 > λ > -1 </p>
<h6><code><span title="System.double">double</span></code> p</h6>
<p class="comments">First parameter that controls kurtosis. Range: p > 0 </p>
<h6><code><span title="System.double">double</span></code> q</h6>
<p class="comments">Second parameter that controls kurtosis. Range: q > 0 </p>
</div>
</div>
</div>
<div id="PDF" class="method">
<h4><span title="System.double">double</span> <strong>PDF</strong>(<span title="System.double">double</span> location, <span title="System.double">double</span> scale, <span title="System.double">double</span> skew, <span title="System.double">double</span> p, <span title="System.double">double</span> q, <span title="System.double">double</span> x)</h4>
<div class="content">Computes the probability density of the distribution (PDF) at x, i.e. ∂P(X ≤ x)/∂x.
<div class="parameters">
<h5>Parameters</h5>
<h6><code><span title="System.double">double</span></code> location</h6>
<p class="comments">The location (μ) of the distribution. </p>
<h6><code><span title="System.double">double</span></code> scale</h6>
<p class="comments">The scale (σ) of the distribution. Range: σ > 0. </p>
<h6><code><span title="System.double">double</span></code> skew</h6>
<p class="comments">The skew, 1 > λ > -1 </p>
<h6><code><span title="System.double">double</span></code> p</h6>
<p class="comments">First parameter that controls kurtosis. Range: p > 0 </p>
<h6><code><span title="System.double">double</span></code> q</h6>
<p class="comments">Second parameter that controls kurtosis. Range: q > 0 </p>
<h6><code><span title="System.double">double</span></code> x</h6>
<p class="comments">The location at which to compute the density. </p>
</div>
<div class="return">
<h5>Return</h5>
<h6><code><span title="System.double">double</span></code></h6>
<p>the density at <var>x</var>. </p>
</div>
</div>
</div>
<div id="PDFLn" class="method">
<h4><span title="System.double">double</span> <strong>PDFLn</strong>(<span title="System.double">double</span> location, <span title="System.double">double</span> scale, <span title="System.double">double</span> skew, <span title="System.double">double</span> p, <span title="System.double">double</span> q, <span title="System.double">double</span> x)</h4>
<div class="content">Computes the log probability density of the distribution (lnPDF) at x, i.e. ln(∂P(X ≤ x)/∂x).
<div class="parameters">
<h5>Parameters</h5>
<h6><code><span title="System.double">double</span></code> location</h6>
<p class="comments">The location (μ) of the distribution. </p>
<h6><code><span title="System.double">double</span></code> scale</h6>
<p class="comments">The scale (σ) of the distribution. Range: σ > 0. </p>
<h6><code><span title="System.double">double</span></code> skew</h6>
<p class="comments">The skew, 1 > λ > -1 </p>
<h6><code><span title="System.double">double</span></code> p</h6>
<p class="comments">First parameter that controls kurtosis. Range: p > 0 </p>
<h6><code><span title="System.double">double</span></code> q</h6>
<p class="comments">Second parameter that controls kurtosis. Range: q > 0 </p>
<h6><code><span title="System.double">double</span></code> x</h6>
<p class="comments">The location at which to compute the density. </p>
</div>
<div class="return">
<h5>Return</h5>
<h6><code><span title="System.double">double</span></code></h6>
<p>the density at <var>x</var>. </p>
</div>
</div>
</div>
<div id="Sample" class="method">
<h4><span title="System.double">double</span> <strong>Sample</strong>(<span title="System.Random">Random</span> rnd, <span title="System.double">double</span> location, <span title="System.double">double</span> scale, <span title="System.double">double</span> skew, <span title="System.double">double</span> p, <span title="System.double">double</span> q)</h4>
<div class="content">Generates a sample from the Skew Generalized t-distribution.
<div class="parameters">
<h5>Parameters</h5>
<h6><code><span title="System.Random">Random</span></code> rnd</h6>
<p class="comments">The random number generator to use. </p>
<h6><code><span title="System.double">double</span></code> location</h6>
<p class="comments">The location (μ) of the distribution. </p>
<h6><code><span title="System.double">double</span></code> scale</h6>
<p class="comments">The scale (σ) of the distribution. Range: σ > 0. </p>
<h6><code><span title="System.double">double</span></code> skew</h6>
<p class="comments">The skew, 1 > λ > -1 </p>
<h6><code><span title="System.double">double</span></code> p</h6>
<p class="comments">First parameter that controls kurtosis. Range: p > 0 </p>
<h6><code><span title="System.double">double</span></code> q</h6>
<p class="comments">Second parameter that controls kurtosis. Range: q > 0 </p>
</div>
<div class="return">
<h5>Return</h5>
<h6><code><span title="System.double">double</span></code></h6>
<p>a sample from the distribution. </p>
</div>
</div>
</div>
<div id="Sample" class="method">
<h4><span title="System.double">double</span> <strong>Sample</strong>(<span title="System.double">double</span> location, <span title="System.double">double</span> scale, <span title="System.double">double</span> skew, <span title="System.double">double</span> p, <span title="System.double">double</span> q)</h4>
<div class="content">Generates a sample from the Skew Generalized t-distribution.
<div class="parameters">
<h5>Parameters</h5>
<h6><code><span title="System.double">double</span></code> location</h6>
<p class="comments">The location (μ) of the distribution. </p>
<h6><code><span title="System.double">double</span></code> scale</h6>
<p class="comments">The scale (σ) of the distribution. Range: σ > 0. </p>
<h6><code><span title="System.double">double</span></code> skew</h6>
<p class="comments">The skew, 1 > λ > -1 </p>
<h6><code><span title="System.double">double</span></code> p</h6>
<p class="comments">First parameter that controls kurtosis. Range: p > 0 </p>
<h6><code><span title="System.double">double</span></code> q</h6>
<p class="comments">Second parameter that controls kurtosis. Range: q > 0 </p>
</div>
<div class="return">
<h5>Return</h5>
<h6><code><span title="System.double">double</span></code></h6>
<p>a sample from the distribution. </p>
</div>
</div>
</div>
<div id="Samples" class="method">
<h4><span title="System.void">void</span> <strong>Samples</strong>(<span title="System.Double[]">Double[]</span> values, <span title="System.double">double</span> location, <span title="System.double">double</span> scale, <span title="System.double">double</span> skew, <span title="System.double">double</span> p, <span title="System.double">double</span> q)</h4>
<div class="content">Fills an array with samples from the Skew Generalized t-distribution using inverse transform.
<div class="parameters">
<h5>Parameters</h5>
<h6><code><span title="System.Double[]">Double[]</span></code> values</h6>
<p class="comments">The array to fill with the samples. </p>
<h6><code><span title="System.double">double</span></code> location</h6>
<p class="comments">The location (μ) of the distribution. </p>
<h6><code><span title="System.double">double</span></code> scale</h6>
<p class="comments">The scale (σ) of the distribution. Range: σ > 0. </p>
<h6><code><span title="System.double">double</span></code> skew</h6>
<p class="comments">The skew, 1 > λ > -1 </p>
<h6><code><span title="System.double">double</span></code> p</h6>
<p class="comments">First parameter that controls kurtosis. Range: p > 0 </p>
<h6><code><span title="System.double">double</span></code> q</h6>
<p class="comments">Second parameter that controls kurtosis. Range: q > 0 </p>
</div>
<div class="return">
<h5>Return</h5>
<h6><code><span title="System.void">void</span></code></h6>
<p>a sequence of samples from the distribution. </p>
</div>
</div>
</div>
<div id="Samples" class="method">
<h4><span title="System.void">void</span> <strong>Samples</strong>(<span title="System.Random">Random</span> rnd, <span title="System.Double[]">Double[]</span> values, <span title="System.double">double</span> location, <span title="System.double">double</span> scale, <span title="System.double">double</span> skew, <span title="System.double">double</span> p, <span title="System.double">double</span> q)</h4>
<div class="content">Fills an array with samples from the Skew Generalized t-distribution using inverse transform.
<div class="parameters">
<h5>Parameters</h5>
<h6><code><span title="System.Random">Random</span></code> rnd</h6>
<p class="comments">The random number generator to use. </p>
<h6><code><span title="System.Double[]">Double[]</span></code> values</h6>
<p class="comments">The array to fill with the samples. </p>
<h6><code><span title="System.double">double</span></code> location</h6>
<p class="comments">The location (μ) of the distribution. </p>
<h6><code><span title="System.double">double</span></code> scale</h6>
<p class="comments">The scale (σ) of the distribution. Range: σ > 0. </p>
<h6><code><span title="System.double">double</span></code> skew</h6>
<p class="comments">The skew, 1 > λ > -1 </p>
<h6><code><span title="System.double">double</span></code> p</h6>
<p class="comments">First parameter that controls kurtosis. Range: p > 0 </p>
<h6><code><span title="System.double">double</span></code> q</h6>
<p class="comments">Second parameter that controls kurtosis. Range: q > 0 </p>
</div>
<div class="return">
<h5>Return</h5>
<h6><code><span title="System.void">void</span></code></h6>
<p>a sequence of samples from the distribution. </p>
</div>
</div>
</div>
<div id="Samples" class="method">
<h4><span title="System.Collections.Generic.IEnumerable<double>">IEnumerable&lt;double&gt;</span> <strong>Samples</strong>(<span title="System.Random">Random</span> rnd, <span title="System.double">double</span> location, <span title="System.double">double</span> scale, <span title="System.double">double</span> skew, <span title="System.double">double</span> p, <span title="System.double">double</span> q)</h4>
<div class="content">Generates a sequence of samples from the Skew Generalized t-distribution using inverse transform.
<div class="parameters">
<h5>Parameters</h5>
<h6><code><span title="System.Random">Random</span></code> rnd</h6>
<p class="comments">The random number generator to use. </p>
<h6><code><span title="System.double">double</span></code> location</h6>
<p class="comments">The location (μ) of the distribution. </p>
<h6><code><span title="System.double">double</span></code> scale</h6>
<p class="comments">The scale (σ) of the distribution. Range: σ > 0. </p>
<h6><code><span title="System.double">double</span></code> skew</h6>
<p class="comments">The skew, 1 > λ > -1 </p>
<h6><code><span title="System.double">double</span></code> p</h6>
<p class="comments">First parameter that controls kurtosis. Range: p > 0 </p>
<h6><code><span title="System.double">double</span></code> q</h6>
<p class="comments">Second parameter that controls kurtosis. Range: q > 0 </p>
</div>
<div class="return">
<h5>Return</h5>
<h6><code><span title="System.Collections.Generic.IEnumerable<double>">IEnumerable&lt;double&gt;</span></code></h6>
<p>a sequence of samples from the distribution. </p>
</div>
</div>
</div>
<div id="Samples" class="method">
<h4><span title="System.Collections.Generic.IEnumerable<double>">IEnumerable&lt;double&gt;</span> <strong>Samples</strong>(<span title="System.double">double</span> location, <span title="System.double">double</span> scale, <span title="System.double">double</span> skew, <span title="System.double">double</span> p, <span title="System.double">double</span> q)</h4>
<div class="content">Generates a sequence of samples from the Skew Generalized t-distribution using inverse transform.
<div class="parameters">
<h5>Parameters</h5>
<h6><code><span title="System.double">double</span></code> location</h6>
<p class="comments">The location (μ) of the distribution. </p>
<h6><code><span title="System.double">double</span></code> scale</h6>
<p class="comments">The scale (σ) of the distribution. Range: σ > 0. </p>
<h6><code><span title="System.double">double</span></code> skew</h6>
<p class="comments">The skew, 1 > λ > -1 </p>
<h6><code><span title="System.double">double</span></code> p</h6>
<p class="comments">First parameter that controls kurtosis. Range: p > 0 </p>
<h6><code><span title="System.double">double</span></code> q</h6>
<p class="comments">Second parameter that controls kurtosis. Range: q > 0 </p>
</div>
<div class="return">
<h5>Return</h5>
<h6><code><span title="System.Collections.Generic.IEnumerable<double>">IEnumerable&lt;double&gt;</span></code></h6>
<p>a sequence of samples from the distribution. </p>
</div>
</div>
</div>
<h3 class="section">Public Methods</h3>
<div id="CumulativeDistribution" class="method">
<h4><span title="System.double">double</span> <strong>CumulativeDistribution</strong>(<span title="System.double">double</span> x)</h4>
<div class="content">
</div>
</div>
<div id="Density" class="method">
<h4><span title="System.double">double</span> <strong>Density</strong>(<span title="System.double">double</span> x)</h4>
<div class="content">
</div>
</div>
<div id="DensityLn" class="method">
<h4><span title="System.double">double</span> <strong>DensityLn</strong>(<span title="System.double">double</span> x)</h4>
<div class="content">
</div>
</div>
<div id="Equals" class="method">
<h4><span title="System.bool">bool</span> <strong>Equals</strong>(<span title="System.object">object</span> obj)</h4>
<div class="content">
</div>
</div>
<div id="GetHashCode" class="method">
<h4><span title="System.int">int</span> <strong>GetHashCode</strong>()</h4>
<div class="content">
</div>
</div>
<div id="GetType" class="method">
<h4><span title="System.Type">Type</span> <strong>GetType</strong>()</h4>
<div class="content">
</div>
</div>
<div id="InverseCumulativeDistribution" class="method">
<h4><span title="System.double">double</span> <strong>InverseCumulativeDistribution</strong>(<span title="System.double">double</span> p)</h4>
<div class="content">Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
at the given probability. This is also known as the quantile or percent point function.
<div class="parameters">
<h5>Parameters</h5>
<h6><code><span title="System.double">double</span></code> p</h6>
<p class="comments">The location at which to compute the inverse cumulative density. </p>
</div>
<div class="return">
<h5>Return</h5>
<h6><code><span title="System.double">double</span></code></h6>
<p>the inverse cumulative density at <var>p</var>. </p>
</div>
</div>
</div>
<div id="Sample" class="method">
<h4><span title="System.double">double</span> <strong>Sample</strong>()</h4>
<div class="content">
</div>
</div>
<div id="Samples" class="method">
<h4><span title="System.Collections.Generic.IEnumerable<double>">IEnumerable&lt;double&gt;</span> <strong>Samples</strong>()</h4>
<div class="content">
</div>
</div>
<div id="Samples" class="method">
<h4><span title="System.void">void</span> <strong>Samples</strong>(<span title="System.Double[]">Double[]</span> values)</h4>
<div class="content">
</div>
</div>
<div id="ToString" class="method">
<h4><span title="System.string">string</span> <strong>ToString</strong>()</h4>
<div class="content">A string representation of the distribution.
<div class="return">
<h5>Return</h5>
<h6><code><span title="System.string">string</span></code></h6>
<p>a string representation of the distribution. </p>
</div>
</div>
</div>
<h3 class="section">Public Properties</h3>
<div id="Entropy" class="method">
<h4><span title="System.double">double</span> <strong>Entropy</strong> get; </h4>
<div class="content">
</div>
</div>
<div id="Location" class="method">
<h4><span title="System.double">double</span> <strong>Location</strong> get; </h4>
<div class="content">Gets the location (μ) of the Skewed Generalized t-distribution.
</div>
</div>
<div id="Maximum" class="method">
<h4><span title="System.double">double</span> <strong>Maximum</strong> get; </h4>
<div class="content">
</div>
</div>
<div id="Mean" class="method">
<h4><span title="System.double">double</span> <strong>Mean</strong> get; </h4>
<div class="content">
</div>
</div>
<div id="Median" class="method">
<h4><span title="System.double">double</span> <strong>Median</strong> get; </h4>
<div class="content">
</div>
</div>
<div id="Minimum" class="method">
<h4><span title="System.double">double</span> <strong>Minimum</strong> get; </h4>
<div class="content">
</div>
</div>
<div id="Mode" class="method">
<h4><span title="System.double">double</span> <strong>Mode</strong> get; </h4>
<div class="content">
</div>
</div>
<div id="P" class="method">
<h4><span title="System.double">double</span> <strong>P</strong> get; </h4>
<div class="content">Gets the first parameter that controls the kurtosis of the distribution. Range: p > 0.
</div>
</div>
<div id="Q" class="method">
<h4><span title="System.double">double</span> <strong>Q</strong> get; </h4>
<div class="content">Gets the second parameter that controls the kurtosis of the distribution. Range: q > 0.
</div>
</div>
<div id="RandomSource" class="method">
<h4><span title="System.Random">Random</span> <strong>RandomSource</strong> get; set;</h4>
<div class="content">Gets or sets the random number generator which is used to draw random samples.
</div>
</div>
<div id="Scale" class="method">
<h4><span title="System.double">double</span> <strong>Scale</strong> get; </h4>
<div class="content">Gets the scale (σ) of the Skewed Generalized t-distribution. Range: σ > 0.
</div>
</div>
<div id="Skew" class="method">
<h4><span title="System.double">double</span> <strong>Skew</strong> get; </h4>
<div class="content">Gets the skew (λ) of the Skewed Generalized t-distribution. Range: 1 > λ > -1.
</div>
</div>
<div id="Skewness" class="method">
<h4><span title="System.double">double</span> <strong>Skewness</strong> get; </h4>
<div class="content">
</div>
</div>
<div id="StdDev" class="method">
<h4><span title="System.double">double</span> <strong>StdDev</strong> get; </h4>
<div class="content">
</div>
</div>
<div id="Variance" class="method">
<h4><span title="System.double">double</span> <strong>Variance</strong> get; </h4>
<div class="content">
</div>
</div>
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@ -237,6 +237,12 @@
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Rayleigh.htm">Rayleigh</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm">SkewedGeneralizedError</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm">SkewedGeneralizedT</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Stable.htm" class="current">Stable</a>
@ -889,7 +895,7 @@ For details about this distribution, see.
</div>
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api/MathNet.Numerics.Distributions/StudentT.htm

@ -237,6 +237,12 @@
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<li>
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<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm">SkewedGeneralizedError</a>
</li>
<li>
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</li>
<li>
<a href="../MathNet.Numerics.Distributions/Stable.htm">Stable</a>
@ -924,7 +930,7 @@ at the given probability. This is also known as the quantile or percent point fu
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api/MathNet.Numerics.Distributions/Triangular.htm

@ -237,6 +237,12 @@
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</li>
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<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm">SkewedGeneralizedError</a>
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<li>
<a href="../MathNet.Numerics.Distributions/Stable.htm">Stable</a>
@ -892,7 +898,7 @@ at the given probability. This is also known as the quantile or percent point fu
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@ -237,6 +237,12 @@
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<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm">SkewedGeneralizedError</a>
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</li>
<li>
<a href="../MathNet.Numerics.Distributions/Stable.htm">Stable</a>
@ -815,7 +821,7 @@
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</div>
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api/MathNet.Numerics.Distributions/Weibull.htm

@ -237,6 +237,12 @@
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<li>
<a href="../MathNet.Numerics.Distributions/Rayleigh.htm">Rayleigh</a>
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<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm">SkewedGeneralizedError</a>
</li>
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<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm">SkewedGeneralizedT</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Stable.htm">Stable</a>
@ -831,7 +837,7 @@ For details about this distribution, see. <blockquote class="remarks">
</div>
</div>
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@ -237,6 +237,12 @@
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<li>
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</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm">SkewedGeneralizedError</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm">SkewedGeneralizedT</a>
</li>
<li>
<a href="../MathNet.Numerics.Distributions/Stable.htm">Stable</a>
@ -521,7 +527,7 @@ Applied Statistics, Vol. 21, No. 3 (1972), pp. 341-345
</div>
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api/MathNet.Numerics.Distributions/Zipf.htm

@ -237,6 +237,12 @@
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</li>
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</li>
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@ -787,7 +793,7 @@ For details about this distribution, see.
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@ -236,6 +236,12 @@
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<li>
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</li>
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<a href="../MathNet.Numerics.Distributions/Stable.htm">Stable</a>
@ -300,6 +306,8 @@
<li><a href="../MathNet.Numerics.Distributions/Pareto.htm">Pareto</a></li>
<li><a href="../MathNet.Numerics.Distributions/Poisson.htm">Poisson</a></li>
<li><a href="../MathNet.Numerics.Distributions/Rayleigh.htm">Rayleigh</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedError.htm">SkewedGeneralizedError</a></li>
<li><a href="../MathNet.Numerics.Distributions/SkewedGeneralizedT.htm">SkewedGeneralizedT</a></li>
<li><a href="../MathNet.Numerics.Distributions/Stable.htm">Stable</a></li>
<li><a href="../MathNet.Numerics.Distributions/StudentT.htm">StudentT</a></li>
<li><a href="../MathNet.Numerics.Distributions/Triangular.htm">Triangular</a></li>
@ -318,7 +326,7 @@
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api/MathNet.Numerics.Financial/AbsoluteReturnMeasures.htm

@ -196,7 +196,7 @@ and then dividing the total by the number of loss periods. <blockquote class="re
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@ -219,7 +219,7 @@ looks at periods where the investment return was less than average return.
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@ -147,7 +147,7 @@
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@ -984,7 +984,7 @@ The data array needs to be N+2 (if N is even) or N+1 (if N is odd) long in order
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@ -369,7 +369,7 @@
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@ -212,7 +212,7 @@
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@ -330,7 +330,7 @@
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@ -155,7 +155,7 @@
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@ -224,7 +224,7 @@ or derivative discontinuities and no poles inside the interval.
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@ -280,7 +280,7 @@
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@ -406,7 +406,7 @@
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@ -393,7 +393,7 @@
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@ -221,7 +221,7 @@
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@ -159,7 +159,7 @@
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@ -443,7 +443,7 @@ The values are assumed to be sorted ascendingly by x.
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@ -310,7 +310,7 @@ WARNING: Works in-place and can thus causes the data array to be reordered.
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@ -492,7 +492,7 @@ and zero second derivatives at the two boundaries, sorted ascendingly by x.
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@ -303,7 +303,7 @@
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@ -354,7 +354,7 @@
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api/MathNet.Numerics.Interpolation/SplineBoundaryCondition.htm

@ -364,7 +364,7 @@
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api/MathNet.Numerics.Interpolation/StepInterpolation.htm

@ -388,7 +388,7 @@ WARNING: Works in-place and can thus causes the data array to be reordered.
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api/MathNet.Numerics.Interpolation/TransformedInterpolation.htm

@ -302,7 +302,7 @@ WARNING: Works in-place and can thus causes the data array to be reordered and m
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@ -186,7 +186,7 @@
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api/MathNet.Numerics.LinearAlgebra.Complex.Solvers/BiCgStab.htm

@ -271,7 +271,7 @@ solution vector and x is the unknown vector.
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api/MathNet.Numerics.LinearAlgebra.Complex.Solvers/CompositeSolver.htm

@ -265,7 +265,7 @@ solution vector and x is the unknown vector.
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api/MathNet.Numerics.LinearAlgebra.Complex.Solvers/DiagonalPreconditioner.htm

@ -272,7 +272,7 @@ of the matrix diagonal as preconditioning values.
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api/MathNet.Numerics.LinearAlgebra.Complex.Solvers/GpBiCg.htm

@ -291,7 +291,7 @@ before switching over to the <code>BiCgStab</code> algorithm.
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api/MathNet.Numerics.LinearAlgebra.Complex.Solvers/ILU0Preconditioner.htm

@ -273,7 +273,7 @@
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api/MathNet.Numerics.LinearAlgebra.Complex.Solvers/ILUTPPreconditioner.htm

@ -384,7 +384,7 @@ the preconditioner. </p>
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api/MathNet.Numerics.LinearAlgebra.Complex.Solvers/MILU0Preconditioner.htm

@ -298,7 +298,7 @@
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api/MathNet.Numerics.LinearAlgebra.Complex.Solvers/MlkBiCgStab.htm

@ -299,7 +299,7 @@ Krylov sub-space.
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api/MathNet.Numerics.LinearAlgebra.Complex.Solvers/TFQMR.htm

@ -265,7 +265,7 @@ solution vector and x is the unknown vector.
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@ -175,7 +175,7 @@
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api/MathNet.Numerics.LinearAlgebra.Complex/DenseMatrix.htm

@ -4601,7 +4601,7 @@ The maximum number of cells can be configured in the <a href="../MathNet.Numeri
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api/MathNet.Numerics.LinearAlgebra.Complex/DenseVector.htm

@ -2637,7 +2637,7 @@ The maximum number of cells can be configured in the <a href="../MathNet.Numeri
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api/MathNet.Numerics.LinearAlgebra.Complex/DiagonalMatrix.htm

@ -4400,7 +4400,7 @@ The format string is ignored.
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api/MathNet.Numerics.LinearAlgebra.Complex/Matrix.htm

@ -4237,7 +4237,7 @@ The maximum number of cells can be configured in the <a href="../MathNet.Numeri
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@ -4584,7 +4584,7 @@ The format string is ignored.
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@ -2605,7 +2605,7 @@ The format string is ignored.
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@ -2458,7 +2458,7 @@ The format string is ignored.
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@ -167,7 +167,7 @@
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api/MathNet.Numerics.LinearAlgebra.Complex32.Solvers/BiCgStab.htm

@ -271,7 +271,7 @@ solution vector and x is the unknown vector.
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@ -265,7 +265,7 @@ solution vector and x is the unknown vector.
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@ -272,7 +272,7 @@ of the matrix diagonal as preconditioning values.
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