diff --git a/src/Numerics/Distributions/LogNormal.cs b/src/Numerics/Distributions/LogNormal.cs
index a827ecbd..4dd69055 100644
--- a/src/Numerics/Distributions/LogNormal.cs
+++ b/src/Numerics/Distributions/LogNormal.cs
@@ -259,6 +259,7 @@ namespace MathNet.Numerics.Distributions
         /// </summary>
         /// <param name="x">The location at which to compute the density.</param>
         /// <returns>the density at <paramref name="x"/>.</returns>
+        /// <seealso cref="PDF"/>
         public double Density(double x)
         {
             if (x < 0.0)
@@ -275,6 +276,7 @@ namespace MathNet.Numerics.Distributions
         /// </summary>
         /// <param name="x">The location at which to compute the log density.</param>
         /// <returns>the log density at <paramref name="x"/>.</returns>
+        /// <seealso cref="PDFLn"/>
         public double DensityLn(double x)
         {
             if (x < 0.0)
@@ -291,11 +293,24 @@ namespace MathNet.Numerics.Distributions
         /// </summary>
         /// <param name="x">The location at which to compute the cumulative distribution function.</param>
         /// <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
+        /// <seealso cref="CDF"/>
         public double CumulativeDistribution(double x)
         {
-            return x < 0.0
-                ? 0.0
-                : 0.5*(1.0 + SpecialFunctions.Erf((Math.Log(x) - _mu)/(_sigma*Constants.Sqrt2)));
+            return x < 0.0 ? 0.0
+                : 0.5*SpecialFunctions.Erfc((_mu - Math.Log(x))/(_sigma*Constants.Sqrt2));
+        }
+
+        /// <summary>
+        /// Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
+        /// at the given probability. This is also known as the 'quantile function'.
+        /// </summary>
+        /// <param name="p">The location at which to compute the inverse cumulative density.</param>
+        /// <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
+        /// <seealso cref="InvCDF"/>
+        public double InverseCumulativeDistribution(double p)
+        {
+            return p <= 0.0 ? 0.0 : p >= 1.0 ? double.PositiveInfinity
+                : Math.Exp(_mu - _sigma*Constants.Sqrt2*SpecialFunctions.ErfcInv(2.0*p));
         }
 
         /// <summary>
@@ -323,9 +338,10 @@ namespace MathNet.Numerics.Distributions
         /// <param name="mu">The log-scale (μ) of the distribution.</param>
         /// <param name="sigma">The shape (σ) of the distribution. Range: σ ≥ 0.</param>
         /// <returns>the density at <paramref name="x"/>.</returns>
+        /// <seealso cref="Density"/>
         public static double PDF(double mu, double sigma, double x)
         {
-            if (sigma < 0.0) throw new ArgumentOutOfRangeException(Resources.InvalidDistributionParameters);
+            if (sigma < 0.0) throw new ArgumentOutOfRangeException("sigma", Resources.InvalidDistributionParameters);
 
             if (x < 0.0)
             {
@@ -343,9 +359,10 @@ namespace MathNet.Numerics.Distributions
         /// <param name="mu">The log-scale (μ) of the distribution.</param>
         /// <param name="sigma">The shape (σ) of the distribution. Range: σ ≥ 0.</param>
         /// <returns>the log density at <paramref name="x"/>.</returns>
+        /// <seealso cref="DensityLn"/>
         public static double PDFLn(double mu, double sigma, double x)
         {
-            if (sigma < 0.0) throw new ArgumentOutOfRangeException(Resources.InvalidDistributionParameters);
+            if (sigma < 0.0) throw new ArgumentOutOfRangeException("sigma", Resources.InvalidDistributionParameters);
 
             if (x < 0.0)
             {
@@ -363,15 +380,32 @@ namespace MathNet.Numerics.Distributions
         /// <param name="mu">The log-scale (μ) of the distribution.</param>
         /// <param name="sigma">The shape (σ) of the distribution. Range: σ ≥ 0.</param>
         /// <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
+        /// <seealso cref="CumulativeDistribution"/>
         public static double CDF(double mu, double sigma, double x)
         {
-            if (sigma < 0.0) throw new ArgumentOutOfRangeException(Resources.InvalidDistributionParameters);
+            if (sigma < 0.0) throw new ArgumentOutOfRangeException("sigma", Resources.InvalidDistributionParameters);
 
-            return x < 0.0
-                ? 0.0
+            return x < 0.0 ? 0.0
                 : 0.5*(1.0 + SpecialFunctions.Erf((Math.Log(x) - mu)/(sigma*Constants.Sqrt2)));
         }
 
+        /// <summary>
+        /// Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
+        /// at the given probability. This is also known as the 'quantile function'.
+        /// </summary>
+        /// <param name="p">The location at which to compute the inverse cumulative density.</param>
+        /// <param name="mu">The log-scale (μ) of the distribution.</param>
+        /// <param name="sigma">The shape (σ) of the distribution. Range: σ ≥ 0.</param>
+        /// <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
+        /// <seealso cref="InverseCumulativeDistribution"/>
+        public static double InvCDF(double mu, double sigma, double p)
+        {
+            if (sigma < 0.0) throw new ArgumentOutOfRangeException("sigma", Resources.InvalidDistributionParameters);
+
+            return p <= 0.0 ? 0.0 : p >= 1.0 ? double.PositiveInfinity
+                : Math.Exp(mu - sigma*Constants.Sqrt2*SpecialFunctions.ErfcInv(2.0*p));
+        }
+
         /// <summary>
         /// Generates a sample from the log-normal distribution using the <i>Box-Muller</i> algorithm.
         /// </summary>
diff --git a/src/Numerics/Distributions/Normal.cs b/src/Numerics/Distributions/Normal.cs
index 737098b4..f7248882 100644
--- a/src/Numerics/Distributions/Normal.cs
+++ b/src/Numerics/Distributions/Normal.cs
@@ -279,6 +279,7 @@ namespace MathNet.Numerics.Distributions
         /// </summary>
         /// <param name="x">The location at which to compute the density.</param>
         /// <returns>the density at <paramref name="x"/>.</returns>
+        /// <seealso cref="PDF"/>
         public double Density(double x)
         {
             var d = (x - _mean)/_stdDev;
@@ -290,6 +291,7 @@ namespace MathNet.Numerics.Distributions
         /// </summary>
         /// <param name="x">The location at which to compute the log density.</param>
         /// <returns>the log density at <paramref name="x"/>.</returns>
+        /// <seealso cref="PDFLn"/>
         public double DensityLn(double x)
         {
             var d = (x - _mean)/_stdDev;
@@ -301,17 +303,19 @@ namespace MathNet.Numerics.Distributions
         /// </summary>
         /// <param name="x">The location at which to compute the cumulative distribution function.</param>
         /// <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
+        /// <seealso cref="CDF"/>
         public double CumulativeDistribution(double x)
         {
-            return 0.5*(1.0 + SpecialFunctions.Erf((x - _mean)/(_stdDev*Constants.Sqrt2)));
+            return 0.5*SpecialFunctions.Erfc((_mean - x)/(_stdDev*Constants.Sqrt2));
         }
 
         /// <summary>
         /// Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
-        /// at the given probability.
+        /// at the given probability. This is also known as the 'quantile function'.
         /// </summary>
         /// <param name="p">The location at which to compute the inverse cumulative density.</param>
         /// <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
+        /// <seealso cref="InvCDF"/>
         public double InverseCumulativeDistribution(double p)
         {
             return _mean - (_stdDev*Constants.Sqrt2*SpecialFunctions.ErfcInv(2.0*p));
@@ -368,6 +372,7 @@ namespace MathNet.Numerics.Distributions
         /// <param name="stddev">The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.</param>
         /// <param name="x">The location at which to compute the density.</param>
         /// <returns>the density at <paramref name="x"/>.</returns>
+        /// <seealso cref="Density"/>
         public static double PDF(double mean, double stddev, double x)
         {
             if (stddev < 0.0) throw new ArgumentOutOfRangeException("stddev", Resources.InvalidDistributionParameters);
@@ -383,6 +388,7 @@ namespace MathNet.Numerics.Distributions
         /// <param name="stddev">The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.</param>
         /// <param name="x">The location at which to compute the density.</param>
         /// <returns>the log density at <paramref name="x"/>.</returns>
+        /// <seealso cref="DensityLn"/>
         public static double PDFLn(double mean, double stddev, double x)
         {
             if (stddev < 0.0) throw new ArgumentOutOfRangeException("stddev", Resources.InvalidDistributionParameters);
@@ -398,6 +404,7 @@ namespace MathNet.Numerics.Distributions
         /// <param name="mean">The mean (μ) of the normal distribution.</param>
         /// <param name="stddev">The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.</param>
         /// <returns>the cumulative distribution at location <paramref name="x"/>.</returns>
+        /// <seealso cref="CumulativeDistribution"/>
         public static double CDF(double mean, double stddev, double x)
         {
             if (stddev < 0.0) throw new ArgumentOutOfRangeException("stddev", Resources.InvalidDistributionParameters);
@@ -407,12 +414,13 @@ namespace MathNet.Numerics.Distributions
 
         /// <summary>
         /// Computes the inverse of the cumulative distribution function (InvCDF) for the distribution
-        /// at the given probability.
+        /// at the given probability. This is also known as the 'quantile function'.
         /// </summary>
         /// <param name="p">The location at which to compute the inverse cumulative density.</param>
         /// <param name="mean">The mean (μ) of the normal distribution.</param>
         /// <param name="stddev">The standard deviation (σ) of the normal distribution. Range: σ ≥ 0.</param>
         /// <returns>the inverse cumulative density at <paramref name="p"/>.</returns>
+        /// <seealso cref="InverseCumulativeDistribution"/>
         public static double InvCDF(double mean, double stddev, double p)
         {
             if (stddev < 0.0) throw new ArgumentOutOfRangeException("stddev", Resources.InvalidDistributionParameters);
diff --git a/src/UnitTests/DistributionTests/Continuous/LogNormalTests.cs b/src/UnitTests/DistributionTests/Continuous/LogNormalTests.cs
index 84167ef5..eee3f768 100644
--- a/src/UnitTests/DistributionTests/Continuous/LogNormalTests.cs
+++ b/src/UnitTests/DistributionTests/Continuous/LogNormalTests.cs
@@ -523,6 +523,40 @@ namespace MathNet.Numerics.UnitTests.DistributionTests.Continuous
             AssertHelpers.AlmostEqual(f, LogNormal.CDF(mu, sigma, x), 8);
         }
 
+        /// <summary>
+        /// Validate inverse cumulative distribution.
+        /// </summary>
+        /// <param name="mu">Mu parameter.</param>
+        /// <param name="sigma">Sigma value.</param>
+        /// <param name="x">Input X value.</param>
+        /// <param name="f">Expected value.</param>
+        [TestCase(-0.100000, 0.100000, 0.500000, 0.0000000015011556178148777579869633555518882664666520593658)]
+        [TestCase(-0.100000, 0.100000, 0.800000, 0.10908001076375810900224507908874442583171381706127)]
+        [TestCase(-0.100000, 1.500000, 0.100000, 0.070999149762464508991968731574953594549291668468349)]
+        [TestCase(-0.100000, 1.500000, 0.500000, 0.34626224992888089297789445771047690175505847991946)]
+        [TestCase(-0.100000, 1.500000, 0.800000, 0.46728530589487698517090261668589508746353129242404)]
+        [TestCase(-0.100000, 2.500000, 0.100000, 0.18914969879695093477606645992572208111152994999076)]
+        [TestCase(-0.100000, 2.500000, 0.500000, 0.40622798321378106125020505907901206714868922279347)]
+        [TestCase(-0.100000, 2.500000, 0.800000, 0.48035707589956665425068652807400957345208517749893)]
+        [TestCase(1.500000, 1.500000, 0.100000, 0.005621455876973168709588070988239748831823850202953)]
+        [TestCase(1.500000, 1.500000, 0.500000, 0.07185716187918271235246980951571040808235628115265)]
+        [TestCase(1.500000, 1.500000, 0.800000, 0.12532699044614938400496547188720940854423187977236)]
+        [TestCase(1.500000, 2.500000, 0.100000, 0.064125647996943514411570834861724406903677144126117)]
+        [TestCase(1.500000, 2.500000, 0.500000, 0.19017302281590810871719754032332631806011441356498)]
+        [TestCase(1.500000, 2.500000, 0.800000, 0.24533064397555500690927047163085419096928289095201)]
+        [TestCase(2.500000, 1.500000, 0.100000, 0.00068304052220788502001572635016579586444611070077399)]
+        [TestCase(2.500000, 1.500000, 0.500000, 0.016636862816580533038130583128179878924863968664206)]
+        [TestCase(2.500000, 1.500000, 0.800000, 0.034729001282904174941366974418836262996834852343018)]
+        [TestCase(2.500000, 2.500000, 0.100000, 0.027363708266690978870139978537188410215717307180775)]
+        [TestCase(2.500000, 2.500000, 0.500000, 0.10075543423327634536450625420610429181921642201567)]
+        [TestCase(2.500000, 2.500000, 0.800000, 0.13802019192453118732001307556787218421918336849121)]
+        public void ValidateInverseCumulativeDistribution(double mu, double sigma, double x, double f)
+        {
+            var n = new LogNormal(mu, sigma);
+            AssertHelpers.AlmostEqual(x, n.InverseCumulativeDistribution(f), 8);
+            AssertHelpers.AlmostEqual(x, LogNormal.InvCDF(mu, sigma, f), 8);
+        }
+
         /// <summary>
         /// Can estimate distribution parameters.
         /// </summary>