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<?xml version="1.0" encoding="UTF-8"?>
<!--
*
* This help file was generated from arburg.sci using help_from_sci().
*
-->
<refentry version="5.0-subset Scilab" xml:id="arburg" xml:lang="en"
xmlns="http://docbook.org/ns/docbook"
xmlns:xlink="http://www.w3.org/1999/xlink"
xmlns:svg="http://www.w3.org/2000/svg"
xmlns:ns3="http://www.w3.org/1999/xhtml"
xmlns:mml="http://www.w3.org/1998/Math/MathML"
xmlns:scilab="http://www.scilab.org"
xmlns:db="http://docbook.org/ns/docbook">
<refnamediv>
<refname>arburg</refname>
<refpurpose>This function calculates coefficients of an autoregressive (AR) model of complex data.</refpurpose>
</refnamediv>
<refsynopsisdiv>
<title>Calling Sequence</title>
<synopsis>
a = arburg(x, poles)
a = arburg(x, poles, criterion)
[a, v] = arburg(...)
[a, v, k] = arburg(...)
</synopsis>
</refsynopsisdiv>
<refsection>
<title>Parameters</title>
<variablelist>
<varlistentry><term>x:</term>
<listitem><para> vector of real or complex numbers, of length > 2</para></listitem></varlistentry>
<varlistentry><term>poles:</term>
<listitem><para> positive integer value < length(x) - 2</para></listitem></varlistentry>
<varlistentry><term>criterion:</term>
<listitem><para> string value, takes in "AKICc", "KIC", "AICc", "AIC" and "FPE", default it not using a model-selection criterion</para></listitem></varlistentry>
<varlistentry><term>a:</term>
<listitem><para> list of autoregression coefficients.</para></listitem></varlistentry>
<varlistentry><term>v:</term>
<listitem><para> mean square of residual noise from the whitening operation of the Burg lattice filter</para></listitem></varlistentry>
<varlistentry><term>k:</term>
<listitem><para> reflection coefficients defining the lattice-filter embodiment of the model</para></listitem></varlistentry>
</variablelist>
</refsection>
<refsection>
<title>Description</title>
<para>
This function calculates coefficients of an autoregressive (AR) model of complex data x using the whitening lattice-filter method of Burg.
</para>
<para>
The first argument is the data sampled. The second argument is the number of poles in the model (or limit in case a criterion is supplied).
The third parameter takes in the criterion to limit the number of poles. The acceptable values are "AIC", "AKICc", "KIC", "AICc" which are based on information theory.
</para>
</refsection>
<refsection>
<title>Examples</title>
<programlisting role="example"><![CDATA[
x = [1,2,3,4,5] ;
poles = 2 ;
arburg(x,poles)
]]></programlisting>
</refsection>
</refentry>
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