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Posted to dev@commons.apache.org by lu...@apache.org on 2007/03/13 21:02:57 UTC

svn commit: r517838 - /jakarta/commons/proper/math/trunk/xdocs/userguide/estimation.xml

Author: luc
Date: Tue Mar 13 13:02:56 2007
New Revision: 517838

URL: http://svn.apache.org/viewvc?view=rev&rev=517838
Log:
added a section for the estimation package in the user guide

Added:
    jakarta/commons/proper/math/trunk/xdocs/userguide/estimation.xml   (with props)

Added: jakarta/commons/proper/math/trunk/xdocs/userguide/estimation.xml
URL: http://svn.apache.org/viewvc/jakarta/commons/proper/math/trunk/xdocs/userguide/estimation.xml?view=auto&rev=517838
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--- jakarta/commons/proper/math/trunk/xdocs/userguide/estimation.xml (added)
+++ jakarta/commons/proper/math/trunk/xdocs/userguide/estimation.xml Tue Mar 13 13:02:56 2007
@@ -0,0 +1,111 @@
+<?xml version="1.0"?>
+
+<!--
+   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.
+  -->
+  
+<?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?>
+<!-- $Revision: 480435 $ $Date: 2006-11-29 08:06:35 +0100 (mer., 29 nov. 2006) $ -->
+<document url="estimation.html">
+
+  <properties>
+    <title>The Commons Math User Guide - Parametric Estimation</title>
+  </properties>
+
+  <body>
+    <section name="12 Parametric Estimation">
+      <subsection name="12.1 Overview" href="overview">
+        <p>
+        The estimation package provides classes to fit some non-linear model
+        to available observations depending on it. These problems are commonly
+        called estimation problems.
+        </p>
+        <p>
+        The estimation problems considered here are parametric problems where
+        a user-provided model depends on initially unknown scalar parameters and
+        several measurements made on values that depend on the model are available.
+        As examples, one can consider the center and radius of a circle given
+        points approximately lying on a ring, or a satellite orbit given range,
+        range-rate and angular measurements from various ground stations.
+        </p>
+        <p>
+        One important class of estimation problems is weighted least squares problems.
+        They basically consist in finding the values for some parameters p<sub>k</sub>
+        such that a cost function J = &sum;(w<sub>i</sub> r<sub>i</sub><sup>2</sup>)
+        is minimized. The various r<sub>i</sub> terms represent the deviation
+        r<sub>i</sub> = mes<sub>i</sub> - mod<sub>i</sub> between the measurements and
+        the parameterized models. The w<sub>i</sub> factors are the measurements weights,
+        they are often chosen either all equal to 1.0 or proportional to the inverse of
+        the variance of the measurement type. The solver adjusts the values of the
+        estimated parameters p<sub>k</sub> which are not bound (i.e. the free parameters).
+        It does not touch the parameters which have been put in a bound state by the user.        
+        </p>
+        <p>
+          The aim of this package is similar to the aim of the optimization package, but the
+          algorithms are entirely differents as:
+          <ul>
+            <li>
+              they need the partial derivatives of the measurements
+              with respect to the free parameters
+            </li>
+            <li>
+              they are residuals based instead of generic cost functions based
+          </ul>
+        </p>
+        
+      </subsection>
+      <subsection name="12.2 Models" href="models">
+        The <a href="../apidocs/org/apache/commons/math/estimation/EstimationProblem.html">
+        org.apache.commons.math.estimation.EstimationProblem</a> interface is a very
+        simple container packing together parameters and measurements.
+      </subsection>
+      <subsection name="12.3 Parameters" href="parameters">
+        <p>
+        The <a href="../apidocs/org/apache/commons/math/estimation/EstimatedParameter.html">
+        org.apache.commons.math.estimation.EstimatedParameter</a> class to represent each
+        estimated parameter. The parameters are set up with a guessed value which will be
+        adjusted by the solver until a best fit is achieved. It is possible to change which
+        parameters are modified and which are preserved thanks to a bound property. Such
+        settings are often needed by expert users to handle contingency cases with very
+        low observability.
+        </p>
+      </subsection>
+      <subsection name="12.4 Measurements" href="measurements">
+        <p>
+        The user extends the <a href="../apidocs/org/apache/commons/math/estimation/WeightedMeasurement.html">
+        org.apache.commons.math.estimation.WeightedMeasurement</a> abstract class to define its
+        own measurements. Each measurement types should have its own implementing class, for
+        example in the satellite example above , the user should define three classes, one
+        for range measurements, one for range-rates measurements and one for angular measurements.
+        Each measurement would correspond to an instance of the appropriate class, set up with
+        the date, a reference to the ground station, the weight and the measured value.
+         </p>
+      </subsection>
+      <subsection name="12.5 Solvers" href="solvers">
+        <p>
+        The package provides two common <a href="../apidocs/org/apache/commons/math/estimation/Estimator.html">
+        org.apache.commons.math.estimation.Estimator</a> implementations to solve weighted
+        least squares problems. The first one is based on the
+        <a href="../apidocs/org/apache/commons/math/estimation/GaussNewtonEstimator.html">Gauss-Newton</a> method.
+        The second one is based on the
+        <a href="../apidocs/org/apache/commons/math/estimation/LevenbergMarquardtEstimator.html">Levenberg-Marquardt</a>
+        method. The first one is best suited when a good approximation of the parameters is known while the second one
+        is more robust and can handle starting points far from the solution.
+        </p>
+      </subsection>
+     </section>
+  </body>
+</document>

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