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SSJ is a Java library for stochastic simulation, developed under the direction of Pierre L'Ecuyer, in the Département d'Informatique et de Recherche Opérationnelle (DIRO), at the Université de Montréal. It provides facilities for generating uniform and nonuniform random variates, computing different measures related to probability distributions, performing goodness-of-fit tests, applying quasi-Monte Carlo methods, collecting (elementary) statistics, and programming discrete-event simulations with both events and processes.

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/*
 * Class:        StudentGen
 * Description:  Student-t random variate generators 
 * Environment:  Java
 * Software:     SSJ 
 * Copyright (C) 2001  Pierre L'Ecuyer and Université de Montréal
 * Organization: DIRO, Université de Montréal
 * @author       
 * @since

 * SSJ is free software: you can redistribute it and/or modify it under
 * the terms of the GNU General Public License (GPL) as published by the
 * Free Software Foundation, either version 3 of the License, or
 * any later version.

 * SSJ is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.

 * A copy of the GNU General Public License is available at
   GPL licence site.
 */

package umontreal.iro.lecuyer.randvar;
import umontreal.iro.lecuyer.rng.*;
import umontreal.iro.lecuyer.probdist.*;


/**
 * This class implements methods for generating random variates from the
 * Student distribution with n > 0 degrees of freedom.
 * Its density function is
 * 
 * 
 * 
 * f (x) = [Γ((n + 1)/2)/(Γ(n/2)(πn)^1/2)][1 + x²/n]^-(n+1)/2                for  - ∞ < x < ∞,
 * 

 * where 
 * Γ(x) is the gamma function defined in
 * {@link GammaGen}.
 * 
 * 
 * The nextDouble method simply calls inverseF on the
 * distribution.
 * 
 * 

 * The following table gives the CPU time needed to generate 10⁷ Student
 *  random variates using the different implementations available in SSJ.
 *  The second test (Q) was made with the inverse in
 *   {@link StudentDistQuick},
 *  while the first test was made with the inverse in {@link StudentDist}.
 * These tests were made on a machine with processor AMD Athlon 4000, running
 * Red Hat Linux, with clock speed at 2400 MHz.
 * 
 * 

 * 

 * 
 * 
 * 
 * 
 * 
 * 
 * 
 * 
 * 
 * 
 * 
 * 
 * 
 * Generator time in seconds
StudentGen 22.4
StudentGen(Q) 6.5
StudentPolarGen 1.4
 * 
 * 
 */
public class StudentGen extends RandomVariateGen  {
   protected int n = -1;


   /**
    * Creates a Student random variate generator with
    *  n degrees of freedom, using stream s.
    * 
    */
   public StudentGen (RandomStream s, int n)  {
      super (s, new StudentDist(n));
      setN (n);
   }


   /**
    * Creates a new generator for the Student distribution dist
    *      and stream s.
    * 
    */
   public StudentGen (RandomStream s, StudentDist dist)  {
      super (s, dist);
      if (dist != null)
         setN (dist.getN ());
   }


   /**
    * Generates a new variate from the Student distribution
    *    with n = n degrees of freedom, using stream s.
    * 
    */
   public static double nextDouble (RandomStream s, int n)  {
      return StudentDist.inverseF (n, s.nextDouble());
    }


   /**
    * Returns the value of n for this object.
    * 
    * 
    */
   public int getN() {
      return n;
   }



   protected void setN (int nu) {
      if (nu <= 0)
         throw new IllegalArgumentException ("n <= 0");
      this.n = nu;
   }
}

Generator	time in seconds
`StudentGen`	22.4
`StudentGen(Q)`	6.5
`StudentPolarGen`	1.4