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diff --git a/src/main/java/org/apache/commons/math/optimization/fitting/GaussianFunction.java b/src/main/java/org/apache/commons/math/optimization/fitting/GaussianFunction.java
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+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.commons.math.optimization.fitting;
+
+import java.io.Serializable;
+
+import org.apache.commons.math.analysis.DifferentiableUnivariateRealFunction;
+import org.apache.commons.math.analysis.UnivariateRealFunction;
+import org.apache.commons.math.exception.DimensionMismatchException;
+import org.apache.commons.math.exception.util.LocalizedFormats;
+import org.apache.commons.math.exception.ZeroException;
+import org.apache.commons.math.exception.NullArgumentException;
+
+/**
+ * A Gaussian function.  Specifically:
+ * <p>
+ * <tt>f(x) = a + b*exp(-((x - c)^2 / (2*d^2)))</tt>
+ * <p>
+ * Notation key:
+ * <ul>
+ * <li><tt>x^n</tt>: <tt>x</tt> raised to the power of <tt>n</tt>
+ * <li><tt>exp(x)</tt>: <i>e</i><tt>^x</tt>
+ * </ul>
+ * References:
+ * <ul>
+ * <li><a href="http://en.wikipedia.org/wiki/Gaussian_function">Wikipedia:
+ *   Gaussian function</a>
+ * </ul>
+ *
+ * @see GaussianDerivativeFunction
+ * @see ParametricGaussianFunction
+ * @since 2.2
+ * @version $Revision: 1037327 $ $Date: 2010-11-20 21:57:37 +0100 (sam. 20 nov. 2010) $
+ */
+public class GaussianFunction implements DifferentiableUnivariateRealFunction, Serializable {
+
+    /** Serializable version identifier. */
+    private static final long serialVersionUID = -3195385616125629512L;
+
+    /** Parameter a of this function. */
+    private final double a;
+
+    /** Parameter b of this function. */
+    private final double b;
+
+    /** Parameter c of this function. */
+    private final double c;
+
+    /** Parameter d of this function. */
+    private final double d;
+
+    /**
+     * Constructs an instance with the specified parameters.
+     *
+     * @param a <tt>a</tt> parameter value
+     * @param b <tt>b</tt> parameter value
+     * @param c <tt>c</tt> parameter value
+     * @param d <tt>d</tt> parameter value
+     *
+     * @throws IllegalArgumentException if <code>d</code> is 0
+     */
+    public GaussianFunction(double a, double b, double c, double d) {
+        if (d == 0.0) {
+            throw new ZeroException();
+        }
+        this.a = a;
+        this.b = b;
+        this.c = c;
+        this.d = d;
+    }
+
+    /**
+     * Constructs an instance with the specified parameters.
+     *
+     * @param parameters <tt>a</tt>, <tt>b</tt>, <tt>c</tt>, and <tt>d</tt>
+     *        parameter values
+     *
+     * @throws IllegalArgumentException if <code>parameters</code> is null,
+     *         <code>parameters</code> length is not 4, or if
+     *         <code>parameters[3]</code> is 0
+     */
+    public GaussianFunction(double[] parameters) {
+        if (parameters == null) {
+            throw new NullArgumentException(LocalizedFormats.INPUT_ARRAY);
+        }
+        if (parameters.length != 4) {
+            throw new DimensionMismatchException(4, parameters.length);
+        }
+        if (parameters[3] == 0.0) {
+            throw new ZeroException();
+        }
+        this.a = parameters[0];
+        this.b = parameters[1];
+        this.c = parameters[2];
+        this.d = parameters[3];
+    }
+
+    /** {@inheritDoc} */
+    public UnivariateRealFunction derivative() {
+        return new GaussianDerivativeFunction(b, c, d);
+    }
+
+    /** {@inheritDoc} */
+    public double value(double x) {
+        final double xMc = x - c;
+        return a + b * Math.exp(-xMc * xMc / (2.0 * (d * d)));
+    }
+
+    /**
+     * Gets <tt>a</tt> parameter value.
+     *
+     * @return <tt>a</tt> parameter value
+     */
+    public double getA() {
+        return a;
+    }
+
+    /**
+     * Gets <tt>b</tt> parameter value.
+     *
+     * @return <tt>b</tt> parameter value
+     */
+    public double getB() {
+        return b;
+    }
+
+    /**
+     * Gets <tt>c</tt> parameter value.
+     *
+     * @return <tt>c</tt> parameter value
+     */
+    public double getC() {
+        return c;
+    }
+
+    /**
+     * Gets <tt>d</tt> parameter value.
+     *
+     * @return <tt>d</tt> parameter value
+     */
+    public double getD() {
+        return d;
+    }
+}