diff --git a/src/Numerics/Distributions/Beta.cs b/src/Numerics/Distributions/Beta.cs
index 8b8a59da..a924f8b2 100644
--- a/src/Numerics/Distributions/Beta.cs
+++ b/src/Numerics/Distributions/Beta.cs
@@ -92,49 +92,6 @@ namespace MathNet.Numerics.Distributions
             _shapeB = b;
         }
 
-
-        /// <summary>
-        /// Create a Beta PERT distribution, used in risk analysis and other domains where an expert forecast
-        /// is used to construct an underlying beta distribution.
-        /// </summary>
-        /// <param name="min">The minimum value.</param>
-        /// <param name="max">The maximum value.</param>
-        /// <param name="likely">The most likely value (mode).</param>
-        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
-        /// <returns>The Beta distribution derived from the PERT parameters.</returns>
-        public static Beta PERT(double min, double max, double likely, System.Random randomSource = null)
-        {
-            if( min > max || likely > max || likely < min )
-            {
-                throw new ArgumentException(Resources.InvalidDistributionParameters);
-            }
-
-            // specified to make the formulas match the literature;
-            // traditionally set to 4 so that the range between min and max
-            // represents six standard deviations (sometimes called
-            // "the six-sigma assumption").
-            const double lambda = 4;
-
-            // calculate the mean
-            double mean = (min + max + lambda * likely) / (lambda + 2);
-
-            // derive the shape parameters a and b
-            double a;
-            // special case where mean and mode are identical
-            if (mean == likely)
-            {
-                a = (lambda/2) + 1;
-            }
-            else
-            {
-                a = ((mean - min) * (2 * likely - min - max)) / ((likely - mean) * (max - min));
-            }
-
-            double b = (a * (max - mean)) / (mean - min);
-
-            return new Beta(a, b, randomSource);
-        }
-
         /// <summary>
         /// A string representation of the distribution.
         /// </summary>
diff --git a/src/Numerics/Distributions/BetaScaled.cs b/src/Numerics/Distributions/BetaScaled.cs
index f7ec5bcc..2f9f004b 100644
--- a/src/Numerics/Distributions/BetaScaled.cs
+++ b/src/Numerics/Distributions/BetaScaled.cs
@@ -88,6 +88,49 @@ namespace MathNet.Numerics.Distributions
             _scale = scale;
         }
 
+        /// <summary>
+        /// Create a Beta PERT distribution, used in risk analysis and other domains where an expert forecast
+        /// is used to construct an underlying beta distribution.
+        /// </summary>
+        /// <param name="min">The minimum value.</param>
+        /// <param name="max">The maximum value.</param>
+        /// <param name="likely">The most likely value (mode).</param>
+        /// <param name="randomSource">The random number generator which is used to draw random samples.</param>
+        /// <returns>The Beta distribution derived from the PERT parameters.</returns>
+        public static BetaScaled PERT(double min, double max, double likely, System.Random randomSource = null)
+        {
+            if (min > max || likely > max || likely < min)
+            {
+                throw new ArgumentException(Resources.InvalidDistributionParameters);
+            }
+
+            // specified to make the formulas match the literature;
+            // traditionally set to 4 so that the range between min and max
+            // represents six standard deviations (sometimes called
+            // "the six-sigma assumption").
+            const double lambda = 4;
+
+            // calculate the mean
+            double mean = (min + max + lambda * likely) / (lambda + 2);
+
+            // derive the shape parameters a and b
+            double a;
+
+            // special case where mean and mode are identical
+            if (mean == likely)
+            {
+                a = (lambda / 2) + 1;
+            }
+            else
+            {
+                a = ((mean - min) * (2 * likely - min - max)) / ((likely - mean) * (max - min));
+            }
+
+            double b = (a * (max - mean)) / (mean - min);
+
+            return new BetaScaled(a, b, min, max - min, randomSource);
+        }
+
         /// <summary>
         /// A string representation of the distribution.
         /// </summary>