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 * This help file was generated from armcov.sci using help_from_sci().
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  <refnamediv>
    <refname>armcov</refname>
    <refpurpose>Autoregressive all-pole model parameters — modified covariance method.</refpurpose>
  </refnamediv>


<refsynopsisdiv>
   <title>Syntax</title>
   <synopsis>
   a = armcov(x, p)
   [a, e] = armcov(x, p)
   
   </synopsis>
</refsynopsisdiv>

<refsection>
   <title>Parameters</title>
   <variablelist>
        <varlistentry>
            <term>x:</term>
            <listitem><para> Input signal.</para></listitem>
        </varlistentry>
        <varlistentry>
            <term>p:</term>
            <listitem><para> Order of the autoregressive model.</para></listitem>
        </varlistentry>
        <varlistentry>
            <term>a:</term>
            <listitem><para> Output of an AR system driven by white noise.</para></listitem>
        </varlistentry>
        <varlistentry>
            <term>e:</term>
            <listitem><para> Variance estimate of the white noise input to the AR model.</para></listitem>
        </varlistentry>
   </variablelist>
</refsection>

<refsection>
   <title>Description</title>
   <para>
This function uses the modified covariance method to fit a pth-order autoregressive (AR) model to the input signal `x`.
   </para>
   <para>
</para>
</refsection>

<refsection>
   <title>Examples</title>
   <programlisting role="example"><![CDATA[
A = [1 -2.7607 3.8106 -2.6535 0.9238]
y = filter(1, A, 0.2 * rand(1024, 1, "normal"))
arcoeffs = armcov(y, 4)
   ]]></programlisting>
</refsection>
</refentry>
