<?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>Calculate coefficients of an autoregressive (AR) model of complex data.</refpurpose>
  </refnamediv>


<refsynopsisdiv>
   <title>Syntax</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 &gt; 2.</para></listitem>
        </varlistentry>
        <varlistentry>
            <term>poles:</term>
            <listitem><para> Positive integer value &lt; length(x) - 2.</para></listitem>
        </varlistentry>
        <varlistentry>
            <term>criterion:</term>
            <listitem><para> String value, takes in "AKICc", "KIC", "AICc", "AIC", and "FPE". Default is not using a model-selection criterion.</para></listitem>
        </varlistentry>
        <varlistentry>
            <term>a:</term>
            <listitem><para> List of P+1 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.
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>
   <para>
</para>
</refsection>

<refsection>
   <title>Examples</title>
   <programlisting role="example"><![CDATA[
arburg([1, 2, 3, 4, 5], 2)

   ]]></programlisting>
</refsection>
</refentry>
