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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:        ChiSquareNoncentralPoisGen
 * Description:  noncentral chi square random variate generators using Poisson
                 and central chi square generators
 * Environment:  Java
 * Software:     SSJ 
 * Copyright (C) 2001  Pierre L'Ecuyer and Université de Montréal
 * Organization: DIRO, Université de Montréal
 * @author       Richard Simard
 * @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 noncentral chi square random variate generators 
 * using Poisson and central chi square generators. It uses the following algorithm:
 * generate a random integer 
 * J∼Poisson(λ/2) from a Poisson 
 * distribution, generate a random real 
 * X∼Γ(j + ν/2, 1/2) from a 
 * gamma distribution, then return X. 
 * Here ν is the number of degrees of freedom and 
 * λ is the noncentrality parameter.
 * 
 * 
 * To generate the Poisson variates, one 
 * uses tabulated inversion for 
 * λ < 10, and the acceptance complement 
 * method for 
 * λ >= 10, as in
 * (see class {@link umontreal.iro.lecuyer.randvar.PoissonTIACGen PoissonTIACGen}).
 * To generate the gamma variates, one 
 * uses acceptance-rejection for α < 1, and acceptance-complement
 * for 
 * α >= 1, as proposed  in
 * (see class {@link umontreal.iro.lecuyer.randvar.GammaAcceptanceRejectionGen GammaAcceptanceRejectionGen}).
 * 
 */
public class ChiSquareNoncentralPoisGen extends ChiSquareNoncentralGen  {
//   protected RandomStream aux; 



   /**
    * Creates a noncentral chi square random variate generator 
    * with ν = nu degrees of freedom and noncentrality parameter
    * λ = lambda using stream stream, as described above.
    * 
    */
   public ChiSquareNoncentralPoisGen (RandomStream stream,
                                      double nu, double lambda)  {
      super (stream, null);
      setParams (nu, lambda);
   }
 
  
   public double nextDouble() {
      return poisGenere (stream, nu, lambda);
   }

   /**
    * Generates a variate from the noncentral chi square
    *    distribution with
    *    parameters ν = nu and λ = lambda using
    *    stream stream, as described above.
    * 
    */
   public static double nextDouble (RandomStream stream,
                                    double nu, double lambda)  {
      return poisGenere (stream, nu, lambda);
   }


//>>>>>>>>>>>>>>>>>>>>  P R I V A T E S    M E T H O D S   <<<<<<<<<<<<<<<<<<<< 

   private static double poisGenere (RandomStream s, double nu, double lambda) {
      int j = PoissonTIACGen.nextInt (s, 0.5*lambda);
      return GammaAcceptanceRejectionGen.nextDouble (s, 0.5*nu + j, 0.5);
   }

}