<para>
        Computes a Zipfian-distributed random integer in <literal>[lb,
        ub]</literal>, see below.
       </para>
       <para>
        <literal>random_zipfian(1, 10, 1.5)</literal>
        <returnvalue>an integer between 1 and 10</returnvalue>
       </para></entry>
      </row>

      <row>
       <entry role="func_table_entry"><para role="func_signature">
        <function>sqrt</function> ( <replaceable>number</replaceable> )
        <returnvalue>double</returnvalue>
       </para>
       <para>
        Square root
       </para>
       <para>
        <literal>sqrt(2.0)</literal>
        <returnvalue>1.414213562</returnvalue>
       </para></entry>
      </row>
     </tbody>
    </tgroup>
   </table>

   <para>
    The <literal>random</literal> function generates values using a uniform
    distribution, that is all the values are drawn within the specified
    range with equal probability. The <literal>random_exponential</literal>,
    <literal>random_gaussian</literal> and <literal>random_zipfian</literal>
    functions require an additional double parameter which determines the precise
    shape of the distribution.
   </para>

   <itemizedlist>
    <listitem>
     <para>
      For an exponential distribution, <replaceable>parameter</replaceable>
      controls the distribution by truncating a quickly-decreasing
      exponential distribution at <replaceable>parameter</replaceable>, and then
      projecting onto integers between the bounds.
      To be precise, with
<literallayout>
f(x) = exp(-parameter * (x - min) / (max - min + 1)) / (1 - exp(-parameter))
</literallayout>
      Then value <replaceable>i</replaceable> between <replaceable>min</replaceable> and
      <replaceable>max</replaceable> inclusive is drawn with probability:
      <literal>f(i) - f(i + 1)</literal>.
     </para>

     <para>
      Intuitively, the larger the <replaceable>parameter</replaceable>, the more
      frequently values close to <replaceable>min</replaceable> are accessed, and the
      less frequently values close to <replaceable>max</replaceable> are accessed.
      The closer to 0 <replaceable>parameter</replaceable> is, the flatter (more
      uniform) the access distribution.
      A crude approximation of the distribution is that the most frequent 1%
      values in the range, close to <replaceable>min</replaceable>, are drawn
      <replaceable>parameter</replaceable>% of the time.
      The <replaceable>parameter</replaceable> value must be strictly positive.
     </para>
    </listitem>

    <listitem>
     <para>
      For a Gaussian distribution, the interval is mapped onto a standard
      normal distribution (the classical bell-shaped Gaussian curve) truncated
      at <literal>-parameter</literal> on the left and <literal>+parameter</literal>
      on the right.
      Values in the middle of the interval are more likely to be drawn.
      To be precise, if <literal>PHI(x)</literal> is the cumulative distribution
      function of the standard normal distribution, with mean <literal>mu</literal>
      defined as <literal>(max + min) / 2.0</literal>, with
<literallayout>
f(x) = PHI(2.0 * parameter * (x - mu) / (max - min + 1)) /
       (2.0 * PHI(parameter) - 1)
</literallayout>
      then value <replaceable>i</replaceable> between <replaceable>min</replaceable> and
      <replaceable>max</replaceable> inclusive is drawn with probability:
      <literal>f(i + 0.5) - f(i - 0.5)</literal>.
      Intuitively, the larger the <replaceable>parameter</replaceable>, the more
      frequently values close to the middle of the interval are drawn, and the
      less frequently values close to the <replaceable>min</replaceable> and
      <replaceable>max</replaceable> bounds. About 67% of values are drawn from the
      middle <literal>1.0 / parameter</literal>, that is a relative
      <literal>0.5 / parameter</literal> around the mean, and 95% in the middle
      <literal>2.0 / parameter</literal>, that is a relative