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<html><head> <meta HTTP-EQUIV="Content-Type" CONTENT="text/html;charset=ISO-8859-1"> <title>Contents.m</title><link rel="stylesheet" type="text/css" href="../stpr.css"></head><body><table border=0 width="100%" cellpadding=0 cellspacing=0><tr valign="baseline"><td valign="baseline" class="function"><b class="function">DEMO_MMGAUSS</b><td valign="baseline" align="right" class="function"><a href="../demos/index.html" target="mdsdir"><img border = 0 src="../up.gif"></a></table> <p><b>Demo on minimax estimation for Gaussian.</b></p> <hr><div class='code'><code><span class=help></span><br><span class=help> <span class=help_field>Synopsis:</span></span><br><span class=help> demo_mmgauss</span><br><span class=help> </span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> demo_mmgauss demonstrates the minimax estimation algorithm </span><br><span class=help> [<a href="../references.html#SH10" title = "M.I.Schlesinger and V.Hlavac. Ten lectures on statistical and structural pattern recognition. Kluwer Academic Publishers, 2002." >SH10</a>] for bivariate Gaussian distribution. The training data </span><br><span class=help> is supposed to contain samples which well describing the </span><br><span class=help> probability distribution function (pdf), i.e., which have </span><br><span class=help> high value of pdf. The samples do not have to be i.i.d. in </span><br><span class=help> contrast to the ML estimation.</span><br><span class=help> </span><br><span class=help> The estimated model is visualized as an ellipsoid:</span><br><span class=help> shape is influenced by the covariance matrix and the center</span><br><span class=help> corresponds to the mean vector.</span><br><span class=help> The lower (red) and upper (blue) bound on the optimal value </span><br><span class=help> of the optimized minimax criterion is displayed at the bottom</span><br><span class=help> part of the window.</span><br><span class=help></span><br><span class=help> <span class=help_field>Control:</span></span><br><span class=help> Epsilon - Stopping condition. The algorithm stops if the </span><br><span class=help> difference between lower and the upper bound</span><br><span class=help> is less then the epsilon.</span><br><span class=help> </span><br><span class=help> Iterations - Number of iterations after which the model </span><br><span class=help> is re-displayed.</span><br><span class=help></span><br><span class=help> FIG2EPS - Exports figure to the PostScript file.</span><br><span class=help> Load data - Loads input data sample from file.</span><br><span class=help> Create data - Invokes program for creating data sample.</span><br><span class=help> Reset - Resets the demo.</span><br><span class=help> Play - Runs the algorithm.</span><br><span class=help> Stop - Stops the running algorithm.</span><br><span class=help> Step - Performs one iteration of the algorithm.</span><br><span class=help> Info - Invokes the info box.</span><br><span class=help> Close - Closes the program.</span><br><span class=help></span><br><span class=help> <span class=also_field>See also </span><span class=also><a href = "../probab/estimation/mmgauss.html" target="mdsbody">MMGAUSS</a>.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../demos/list/demo_mmgauss.html">demo_mmgauss.m</a> <p><b class="info_field">About: </b> Statistical Pattern Recognition Toolbox<br> (C) 1999-2003, Written by Vojtech Franc and Vaclav Hlavac<br> <a href="http://www.cvut.cz">Czech Technical University Prague</a><br> <a href="http://www.feld.cvut.cz">Faculty of Electrical Engineering</a><br> <a href="http://cmp.felk.cvut.cz">Center for Machine Perception</a><br> <p><b class="info_field">Modifications: </b> <br> 2-may-2004, VF<br> 19-sep-2003, VF<br> 3-mar-2003, VF<br> 11-june-2001, V.Franc, comments added.<br> 24. 6.00 V. Hlavac, comments polished.<br></body></html>?? 快捷鍵說明
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