diff --git a/src/Numerics/Statistics/ArrayStatistics.cs b/src/Numerics/Statistics/ArrayStatistics.cs
index 80ff3023..8b0bca39 100644
--- a/src/Numerics/Statistics/ArrayStatistics.cs
+++ b/src/Numerics/Statistics/ArrayStatistics.cs
@@ -163,6 +163,48 @@ namespace MathNet.Numerics.Statistics
             return mean;
         }
 
+        /// <summary>
+        /// Evaluates the geometric mean of the unsorted data array.
+        /// Returns NaN if data is empty or any entry is NaN.
+        /// </summary>
+        /// <param name="data">Sample array, no sorting is assumed.</param>
+        public static double GeometricMean(double[] data)
+        {
+            if (data.Length == 0)
+            {
+                return double.NaN;
+            }
+
+            double sum = 0;
+            for (int i = 0; i < data.Length; i++)
+            {
+                sum += Math.Log(data[i]);
+            }
+
+            return Math.Exp(sum/data.Length);
+        }
+
+        /// <summary>
+        /// Evaluates the harmonic mean of the unsorted data array.
+        /// Returns NaN if data is empty or any entry is NaN.
+        /// </summary>
+        /// <param name="data">Sample array, no sorting is assumed.</param>
+        public static double HarmonicMean(double[] data)
+        {
+            if (data.Length == 0)
+            {
+                return double.NaN;
+            }
+
+            double sum = 0;
+            for (int i = 0; i < data.Length; i++)
+            {
+                sum += 1.0/data[i];
+            }
+
+            return data.Length/sum;
+        }
+
         /// <summary>
         /// Estimates the unbiased population variance from the provided samples as unsorted array.
         /// On a dataset of size N will use an N-1 normalizer (Bessel's correction).
diff --git a/src/Numerics/Statistics/Statistics.cs b/src/Numerics/Statistics/Statistics.cs
index 610a51e4..981d2ef5 100644
--- a/src/Numerics/Statistics/Statistics.cs
+++ b/src/Numerics/Statistics/Statistics.cs
@@ -117,6 +117,34 @@ namespace MathNet.Numerics.Statistics
             return StreamingStatistics.Mean(data.Where(d => d.HasValue).Select(d => d.Value));
         }
 
+        /// <summary>
+        /// Evaluates the geometric mean.
+        /// Returns NaN if data is empty or if any entry is NaN.
+        /// </summary>
+        /// <param name="data">The data to calculate the geometric mean of.</param>
+        /// <returns>The geometric mean of the sample.</returns>
+        public static double GeometricMean(this IEnumerable<double> data)
+        {
+            var array = data as double[];
+            return array != null
+                ? ArrayStatistics.GeometricMean(array)
+                : StreamingStatistics.GeometricMean(data);
+        }
+
+        /// <summary>
+        /// Evaluates the harmonic mean.
+        /// Returns NaN if data is empty or if any entry is NaN.
+        /// </summary>
+        /// <param name="data">The data to calculate the harmonic mean of.</param>
+        /// <returns>The harmonic mean of the sample.</returns>
+        public static double HarmonicMean(this IEnumerable<double> data)
+        {
+            var array = data as double[];
+            return array != null
+                ? ArrayStatistics.HarmonicMean(array)
+                : StreamingStatistics.HarmonicMean(data);
+        }
+
         /// <summary>
         /// Estimates the unbiased population variance from the provided samples.
         /// On a dataset of size N will use an N-1 normalizer (Bessel's correction).
diff --git a/src/Numerics/Statistics/StreamingStatistics.cs b/src/Numerics/Statistics/StreamingStatistics.cs
index d0ba11ec..7ec802bb 100644
--- a/src/Numerics/Statistics/StreamingStatistics.cs
+++ b/src/Numerics/Statistics/StreamingStatistics.cs
@@ -109,6 +109,44 @@ namespace MathNet.Numerics.Statistics
             return any ? mean : double.NaN;
         }
 
+        /// <summary>
+        /// Evaluates the geometric mean of the enumerable, in a single pass without memoization.
+        /// Returns NaN if data is empty or any entry is NaN.
+        /// </summary>
+        /// <param name="stream">Sample stream, no sorting is assumed.</param>
+        public static double GeometricMean(IEnumerable<double> stream)
+        {
+            ulong m = 0;
+            double sum = 0;
+
+            foreach (var d in stream)
+            {
+                sum += Math.Log(d);
+                m++;
+            }
+
+            return m > 0 ? Math.Exp(sum / m) : double.NaN;
+        }
+
+        /// <summary>
+        /// Evaluates the harmonic mean of the enumerable, in a single pass without memoization.
+        /// Returns NaN if data is empty or any entry is NaN.
+        /// </summary>
+        /// <param name="stream">Sample stream, no sorting is assumed.</param>
+        public static double HarmonicMean(IEnumerable<double> stream)
+        {
+            ulong m = 0;
+            double sum = 0;
+
+            foreach (var d in stream)
+            {
+                sum += 1.0 / d;
+                m++;
+            }
+
+            return m > 0 ? m/sum : double.NaN;
+        }
+
         /// <summary>
         /// Estimates the unbiased population variance from the provided samples as enumerable sequence, in a single pass without memoization.
         /// On a dataset of size N will use an N-1 normalizer (Bessel's correction).
diff --git a/src/UnitTests/StatisticsTests/StatisticsTests.cs b/src/UnitTests/StatisticsTests/StatisticsTests.cs
index 64e8023e..3943609f 100644
--- a/src/UnitTests/StatisticsTests/StatisticsTests.cs
+++ b/src/UnitTests/StatisticsTests/StatisticsTests.cs
@@ -4,7 +4,7 @@
 // http://github.com/mathnet/mathnet-numerics
 // http://mathnetnumerics.codeplex.com
 //
-// Copyright (c) 2009-2014 Math.NET
+// Copyright (c) 2009-2015 Math.NET
 //
 // Permission is hereby granted, free of charge, to any person
 // obtaining a copy of this software and associated documentation
@@ -30,7 +30,6 @@
 
 using System;
 using System.Collections.Generic;
-using System.IO;
 using System.Linq;
 using MathNet.Numerics.Distributions;
 using MathNet.Numerics.Random;
@@ -71,6 +70,8 @@ namespace MathNet.Numerics.UnitTests.StatisticsTests
             Assert.That(() => Statistics.Minimum(data), Throws.Exception);
             Assert.That(() => Statistics.Maximum(data), Throws.Exception);
             Assert.That(() => Statistics.Mean(data), Throws.Exception);
+            Assert.That(() => Statistics.HarmonicMean(data), Throws.Exception);
+            Assert.That(() => Statistics.GeometricMean(data), Throws.Exception);
             Assert.That(() => Statistics.Median(data), Throws.Exception);
             Assert.That(() => Statistics.Quantile(data, 0.3), Throws.Exception);
             Assert.That(() => Statistics.Variance(data), Throws.Exception);
@@ -99,6 +100,8 @@ namespace MathNet.Numerics.UnitTests.StatisticsTests
             Assert.That(() => ArrayStatistics.Maximum(data), Throws.Exception.TypeOf<NullReferenceException>());
             Assert.That(() => ArrayStatistics.OrderStatisticInplace(data, 1), Throws.Exception.TypeOf<NullReferenceException>());
             Assert.That(() => ArrayStatistics.Mean(data), Throws.Exception.TypeOf<NullReferenceException>());
+            Assert.That(() => ArrayStatistics.HarmonicMean(data), Throws.Exception.TypeOf<NullReferenceException>());
+            Assert.That(() => ArrayStatistics.GeometricMean(data), Throws.Exception.TypeOf<NullReferenceException>());
             Assert.That(() => ArrayStatistics.Variance(data), Throws.Exception.TypeOf<NullReferenceException>());
             Assert.That(() => ArrayStatistics.StandardDeviation(data), Throws.Exception.TypeOf<NullReferenceException>());
             Assert.That(() => ArrayStatistics.PopulationVariance(data), Throws.Exception.TypeOf<NullReferenceException>());
@@ -112,6 +115,8 @@ namespace MathNet.Numerics.UnitTests.StatisticsTests
             Assert.That(() => StreamingStatistics.Minimum(data), Throws.Exception.TypeOf<NullReferenceException>());
             Assert.That(() => StreamingStatistics.Maximum(data), Throws.Exception.TypeOf<NullReferenceException>());
             Assert.That(() => StreamingStatistics.Mean(data), Throws.Exception.TypeOf<NullReferenceException>());
+            Assert.That(() => StreamingStatistics.HarmonicMean(data), Throws.Exception.TypeOf<NullReferenceException>());
+            Assert.That(() => StreamingStatistics.GeometricMean(data), Throws.Exception.TypeOf<NullReferenceException>());
             Assert.That(() => StreamingStatistics.Variance(data), Throws.Exception.TypeOf<NullReferenceException>());
             Assert.That(() => StreamingStatistics.StandardDeviation(data), Throws.Exception.TypeOf<NullReferenceException>());
             Assert.That(() => StreamingStatistics.PopulationVariance(data), Throws.Exception.TypeOf<NullReferenceException>());
@@ -134,6 +139,8 @@ namespace MathNet.Numerics.UnitTests.StatisticsTests
             Assert.DoesNotThrow(() => Statistics.Minimum(data));
             Assert.DoesNotThrow(() => Statistics.Maximum(data));
             Assert.DoesNotThrow(() => Statistics.Mean(data));
+            Assert.DoesNotThrow(() => Statistics.HarmonicMean(data));
+            Assert.DoesNotThrow(() => Statistics.GeometricMean(data));
             Assert.DoesNotThrow(() => Statistics.Median(data));
             Assert.DoesNotThrow(() => Statistics.Quantile(data, 0.3));
             Assert.DoesNotThrow(() => Statistics.Variance(data));