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  <refnamediv>
    <refname>distfun_wblstat</refname>
    <refpurpose>Weibull  mean and variance</refpurpose>
  </refnamediv>


<refsynopsisdiv>
   <title>Calling Sequence</title>
   <synopsis>
   M = distfun_wblstat ( a , b )
   [M,V] = distfun_wblstat ( a , b )
   
   </synopsis>
</refsynopsisdiv>

<refsection>
   <title>Parameters</title>
   <variablelist>
   <varlistentry><term>a :</term>
      <listitem><para> a matrix of doubles, the scale parameter, a>0.</para></listitem></varlistentry>
   <varlistentry><term>b :</term>
      <listitem><para> a matrix of doubles, the shape parameter, b>0.</para></listitem></varlistentry>
   <varlistentry><term>M :</term>
      <listitem><para> a matrix of doubles, the mean</para></listitem></varlistentry>
   <varlistentry><term>V :</term>
      <listitem><para> a matrix of doubles, the variance</para></listitem></varlistentry>
   </variablelist>
</refsection>

<refsection>
   <title>Description</title>
   <para>
Computes statistics from the Weibull distribution.
   </para>
   <para>
The mean and variance of the Weibull distribution are
   </para>
   <para>
<latex>
\begin{eqnarray}
M &amp;=&amp; a \Gamma\left(1 + \frac{1}{b}\right) \\
V &amp;=&amp; a^2 \Gamma\left(1 + \frac{2}{b}\right)-M^2
\end{eqnarray}
</latex>
   </para>
   <para>
Any scalar input argument is expanded to a matrix of
doubles of the same size as the other input arguments.
   </para>
   <para>
</para>
</refsection>

<refsection>
   <title>Examples</title>
   <programlisting role="example"><![CDATA[
[M,V]=distfun_wblstat(1:3,(1:3).^-1)
me = [1.    4.    18.];
ve = [1.    80.    6156.];

   ]]></programlisting>
</refsection>

<refsection>
   <title>Authors</title>
   <simplelist type="vert">
   <member>Copyright (C) 2014 - Michael Baudin</member>
   </simplelist>
</refsection>
</refentry>
