diff --git a/Sources/Accord.Statistics/Accord.Statistics.csproj b/Sources/Accord.Statistics/Accord.Statistics.csproj
index 76acab3e0..d6a1fc9e0 100644
--- a/Sources/Accord.Statistics/Accord.Statistics.csproj
+++ b/Sources/Accord.Statistics/Accord.Statistics.csproj
@@ -354,6 +354,7 @@
     <Compile Include="Models\Markov\Learning\Base\BaseHiddenMarkovModelLearning.cs" />
     <Compile Include="Models\Markov\Learning\Base\BaseViterbiLearning`1.cs" />
     <Compile Include="Models\Markov\Learning\Base\BaseBaumWelchLearning`3.cs" />
+    <Compile Include="Models\Markov\Learning\BaumWelchLearning`3.cs" />
     <Compile Include="Models\Markov\Learning\BaumWelchLearning`2.cs" />
     <Compile Include="Models\Markov\Learning\HiddenMarkovClassifierLearning`2.cs" />
     <Compile Include="Models\Markov\Learning\Base\BaseMaximumLikelihoodLearning`2.cs" />
diff --git a/Sources/Accord.Statistics/Models/Markov/Learning/BaumWelchLearning`2.cs b/Sources/Accord.Statistics/Models/Markov/Learning/BaumWelchLearning`2.cs
index 8bc231656..2feb52383 100644
--- a/Sources/Accord.Statistics/Models/Markov/Learning/BaumWelchLearning`2.cs
+++ b/Sources/Accord.Statistics/Models/Markov/Learning/BaumWelchLearning`2.cs
@@ -32,52 +32,6 @@ namespace Accord.Statistics.Models.Markov.Learning
     using Accord.MachineLearning;
     using Accord.Statistics.Models.Markov.Topology;
 
-    /// <summary>
-    ///   Baum-Welch learning algorithms for learning Hidden Markov Models.
-    /// </summary>
-    /// 
-    /// <typeparam name="TDistribution">The type of the emission distributions in the model.</typeparam>
-    /// <typeparam name="TObservation">The type of the observations (i.e. int for a discrete model).</typeparam>
-    /// <typeparam name="TOptions">The type of fitting options accepted by this distribution.</typeparam>
-    /// 
-    public class BaumWelchLearning<TDistribution, TObservation, TOptions> :
-        BaseBaumWelchLearningOptions<HiddenMarkovModel<TDistribution, TObservation>, TDistribution, TObservation, TOptions>,
-        IConvergenceLearning
-        where TDistribution : IFittableDistribution<TObservation, TOptions>
-        where TOptions : class, IFittingOptions, new()
-    {
-
-        /// <summary>
-        /// Initializes a new instance of the <see cref="BaumWelchLearning{TDistribution, TObservation, TOptions}"/> class.
-        /// </summary>
-        /// 
-        public BaumWelchLearning()
-        {
-        }
-
-        /// <summary>
-        /// Initializes a new instance of the <see cref="BaumWelchLearning{TDistribution, TObservation, TOptions}"/> class.
-        /// </summary>
-        /// 
-        /// <param name="model">The model to be learned.</param>
-        /// 
-        public BaumWelchLearning(HiddenMarkovModel<TDistribution, TObservation> model)
-            : base(model)
-        {
-        }
-
-        /// <summary>
-        ///   Creates an instance of the model to be learned. Inheritors of this abstract 
-        ///   class must define this method so new models can be created from the training data.
-        /// </summary>
-        /// 
-        protected override HiddenMarkovModel<TDistribution, TObservation> Create()
-        {
-            return new HiddenMarkovModel<TDistribution, TObservation>(Topology, Emissions);
-        }
-
-    }
-
     /// <summary>
     ///   Baum-Welch learning algorithm for <see cref="HiddenMarkovModel{TDistribution}">
     ///   arbitrary-density (generic) Hidden Markov Models</see>.
@@ -184,7 +138,7 @@ protected override HiddenMarkovModel<TDistribution, TObservation> Create()
     /// <seealso cref="HiddenMarkovModel"/>
     /// <seealso cref="HiddenMarkovModel{TDistribution, TObservation}"/>
     /// <seealso cref="BaumWelchLearning"/>
-    /// <seealso cref="BaumWelchLearning{TDistribution, TObservation}"/>
+    /// <seealso cref="BaumWelchLearning{TDistribution, TObservation, TOptions}"/>
     /// 
     /// <typeparam name="TDistribution">The type of the emission distributions in the model.</typeparam>
     /// <typeparam name="TObservation">The type of the observations (i.e. int for a discrete model).</typeparam>
diff --git a/Sources/Accord.Statistics/Models/Markov/Learning/BaumWelchLearning`3.cs b/Sources/Accord.Statistics/Models/Markov/Learning/BaumWelchLearning`3.cs
new file mode 100644
index 0000000000..cc27bdb9e
--- /dev/null
+++ b/Sources/Accord.Statistics/Models/Markov/Learning/BaumWelchLearning`3.cs
@@ -0,0 +1,79 @@
+// Accord Statistics Library
+// The Accord.NET Framework
+// http://accord-framework.net
+//
+// Copyright © César Souza, 2009-2017
+// cesarsouza at gmail.com
+//
+//    This library is free software; you can redistribute it and/or
+//    modify it under the terms of the GNU Lesser General Public
+//    License as published by the Free Software Foundation; either
+//    version 2.1 of the License, or (at your option) any later version.
+//
+//    This library is distributed in the hope that it will be useful,
+//    but WITHOUT ANY WARRANTY; without even the implied warranty of
+//    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+//    Lesser General Public License for more details.
+//
+//    You should have received a copy of the GNU Lesser General Public
+//    License along with this library; if not, write to the Free Software
+//    Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301  USA
+//
+
+namespace Accord.Statistics.Models.Markov.Learning
+{
+    using Accord.Statistics.Models.Markov;
+    using Accord.Statistics.Distributions;
+    using Accord.Statistics.Distributions.Fitting;
+
+    /// <summary>
+    ///   Baum-Welch learning algorithms for learning Hidden Markov Models.
+    /// </summary>
+    /// 
+    /// <typeparam name="TDistribution">The type of the emission distributions in the model.</typeparam>
+    /// <typeparam name="TObservation">The type of the observations (i.e. int for a discrete model).</typeparam>
+    /// <typeparam name="TOptions">The type of fitting options accepted by this distribution.</typeparam>
+    /// 
+    /// <remarks>
+    ///   Please see the <see cref="BaumWelchLearning{TDistribution, TObservation}"/> documentation page for
+    ///   the actual documentation of this class, including examples.
+    /// </remarks>
+    /// 
+    public class BaumWelchLearning<TDistribution, TObservation, TOptions> :
+        BaseBaumWelchLearningOptions<HiddenMarkovModel<TDistribution, TObservation>, TDistribution, TObservation, TOptions>,
+        IConvergenceLearning
+        where TDistribution : IFittableDistribution<TObservation, TOptions>
+        where TOptions : class, IFittingOptions, new()
+    {
+
+        /// <summary>
+        /// Initializes a new instance of the <see cref="BaumWelchLearning{TDistribution, TObservation, TOptions}"/> class.
+        /// </summary>
+        /// 
+        public BaumWelchLearning()
+        {
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the <see cref="BaumWelchLearning{TDistribution, TObservation, TOptions}"/> class.
+        /// </summary>
+        /// 
+        /// <param name="model">The model to be learned.</param>
+        /// 
+        public BaumWelchLearning(HiddenMarkovModel<TDistribution, TObservation> model)
+            : base(model)
+        {
+        }
+
+        /// <summary>
+        ///   Creates an instance of the model to be learned. Inheritors of this abstract 
+        ///   class must define this method so new models can be created from the training data.
+        /// </summary>
+        /// 
+        protected override HiddenMarkovModel<TDistribution, TObservation> Create()
+        {
+            return new HiddenMarkovModel<TDistribution, TObservation>(Topology, Emissions);
+        }
+
+    }
+}