RandomVariables
Class HyperGeometricVariable

java.lang.Object
  extended byStatistics.RandomVariable
      extended byRandomVariables.HyperGeometricVariable

public class HyperGeometricVariable
extends RandomVariable

Hypergeomeric HG(N,n,p) variable X. A sample of size n is drawn from a poulation of size N. The population contains s successes. X is the number of successes in the sample.


Constructor Summary
HyperGeometricVariable(int N, int s, int n)
           
 
Method Summary
 double analyticMean()
          Unconditional mean given by exact formula.
 double analyticVariance()
          Unconditional variance given by exact formula.
 cern.jet.random.HyperGeometric getHyperGeometricDistribution()
          The underlying cern.jet.random.HyperGeometric Hypergeometric distribution.
 double getValue(int t)
          A new sample from the distribution of X conditioned on information available at time t.
static void main(java.lang.String[] args)
          Allocates a HyperGeometric(1000,400,300) variable and compares the analytic mean and variance to Monte Carlo mean and variance over a sample of size 100,000.
 
Methods inherited from class Statistics.RandomVariable
analyticCentralMoment, analyticConditionalCentraMoment, analyticConditionalMean, analyticConditionalMoment, analyticConditionalVariance, analyticMoment, basicHistogram, centered_X, conditionalEmpiricalDistribution, conditionalExpectation, conditionalExpectation, conditionalExpectation, conditionalExpectation, conditionalExpectation, conditionalHistogram, conditionalHistogram, conditionalMeanAndStandardDeviation, conditionalMeanAndStandardDeviation, conditionalMeanAndStandardDeviation, conditionalMeanAndStandardDeviation, conditionalMoment, conditionalVariance, cumulativeDistributionFunction, displayConditionalHistogram, displayConditionalHistogram, displayConditionalHistogram, displayConditionalHistogram, displayHistogram, displayHistogram, displayHistogram, displayHistogram, div, empiricalDistribution, expectation, expectation, expectation, expectation, expectation, fillSampleSet, get_empiricalDistributionIsInitialized, get_hasAnalyticCentralMoment, get_hasAnalyticMean, get_hasAnalyticMoment, get_hasAnalyticVariance, get_hasConditionalAnalyticCentralMoment, get_hasConditionalAnalyticMean, get_hasConditionalAnalyticMoment, get_hasConditionalAnalyticVariance, histogram, histogram, initEmpiricalDistribution, meanAndStandardDeviation, meanAndStandardDeviation, meanAndStandardDeviation, meanAndStandardDeviation, minus, mult, plus, quantile, scale, setHasAnalyticMean, setHasAnalyticVariance, variance
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Constructor Detail

HyperGeometricVariable

public HyperGeometricVariable(int N,
                              int s,
                              int n)
Parameters:
N - population size.
s - number of successes in population.
n - sample size.
Method Detail

analyticMean

public double analyticMean()
Description copied from class: RandomVariable

Unconditional mean given by exact formula.

Since in general there are no analytic formulas the default is an error message and program abort. Override this in special cases where analytic formulas do exist.

Overrides:
analyticMean in class RandomVariable

analyticVariance

public double analyticVariance()
Description copied from class: RandomVariable

Unconditional variance given by exact formula.

Since in general there are no analytic formulas the default is an error message and program abort. Override this in special cases where analytic formulas do exist.

Overrides:
analyticVariance in class RandomVariable

getHyperGeometricDistribution

public cern.jet.random.HyperGeometric getHyperGeometricDistribution()
The underlying cern.jet.random.HyperGeometric Hypergeometric distribution. Much additional functionality. See the javdoc of the colt distribution.


getValue

public double getValue(int t)
Description copied from class: RandomVariable

A new sample from the distribution of X conditioned on information available at time t.

This is the crucial method defining the random variable and information structure.

Specified by:
getValue in class RandomVariable
Parameters:
t - time - ingnored no sense of time defined (no conditioning in this context).

main

public static void main(java.lang.String[] args)
Allocates a HyperGeometric(1000,400,300) variable and compares the analytic mean and variance to Monte Carlo mean and variance over a sample of size 100,000. A histogram is shown also.