SSJ
V. 1.2.5.

umontreal.iro.lecuyer.probdist
Class NegativeBinomialDist

java.lang.Object
  extended by umontreal.iro.lecuyer.probdist.DiscreteDistributionInt
      extended by umontreal.iro.lecuyer.probdist.NegativeBinomialDist
All Implemented Interfaces:
Distribution
Direct Known Subclasses:
PascalDist

public class NegativeBinomialDist
extends DiscreteDistributionInt

Extends the class DiscreteDistributionInt for the negative binomial distribution with real parameters γ and p, where γ > 0 and 0 <= p <= 1. Its mass function is

p(x) = Γ(γ + x)/(xΓ(γ))pγ(1 - p)x,        for x = 0, 1, 2,…

where Γ is the gamma function.

If γ is an integer, p(x) can be interpreted as the probability of having x failures before the γ-th success in a sequence of independent Bernoulli trials with probability of success p. This special case is implemented as the Pascal distribution (see PascalDist).


Field Summary
static double MAXN
           
 
Fields inherited from class umontreal.iro.lecuyer.probdist.DiscreteDistributionInt
EPSILON
 
Constructor Summary
NegativeBinomialDist(double gamma, double p)
          Creates an object that contains the probability terms and the distribution function for the negative binomial distribution with parameters γ and p.
 
Method Summary
 double barF(int x)
          Returns bar(F)(x), the complementary distribution function.
static double cdf(double gamma, double p, int x)
          Computes the distribution function.
 double cdf(int x)
          Returns the distribution function F evaluated at x (see).
 double getGamma()
          Returns the parameter γ of this object.
static NegativeBinomialDist getInstanceFromMLE(int[] x, int n)
          Creates a new instance of a negative binomial distribution with parameters γ and p estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
static NegativeBinomialDist getInstanceFromMLE(int[] x, int n, double gamma)
          Creates a new instance of a negative binomial distribution with parameters γ = gamma given and hat(p) estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
static double[] getMaximumLikelihoodEstimate(int[] x, int n)
          Estimates and returns the parameters [ hat(γ), hat(p)] of the negative binomial distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
static double[] getMaximumLikelihoodEstimate(int[] x, int n, double gamma)
          Estimates and returns the parameter [hat(p)] of the negative binomial distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.
 double getMean()
          Returns the mean of the distribution function.
static double getMean(double gamma, double p)
          Computes and returns the mean E[X] = γ(1 - p)/p of the negative binomial distribution with parameters γ and p.
 double getP()
          Returns the parameter p of this object.
 double getStandardDeviation()
          Returns the standard deviation of the distribution function.
static double getStandardDeviation(double gamma, double p)
          Computes and returns the standard deviation of the negative binomial distribution with parameters γ and p.
 double getVariance()
          Returns the variance of the distribution function.
static double getVariance(double gamma, double p)
          Computes and returns the variance Var[X] = γ(1 - p)/p2 of the negative binomial distribution with parameters γ and p.
static int inverseF(double gamma, double p, double u)
          Computes the inverse function without precomputing tables.
 int inverseFInt(double u)
          Returns the inverse distribution function F-1(u), where 0 <= u <= 1.
static double prob(double gamma, double p, int x)
          Computes the probability mass function.
 double prob(int x)
          Returns p(x), the probability of x, which should be a real number in the interval [0, 1].
 void setParams(double gamma, double p)
          Sets the parameter γ and p of this object.
 
Methods inherited from class umontreal.iro.lecuyer.probdist.DiscreteDistributionInt
barF, cdf, inverseF
 
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Field Detail

MAXN

public static double MAXN
Constructor Detail

NegativeBinomialDist

public NegativeBinomialDist(double gamma,
                            double p)
Creates an object that contains the probability terms and the distribution function for the negative binomial distribution with parameters γ and p.

Method Detail

prob

public double prob(int x)
Description copied from class: DiscreteDistributionInt
Returns p(x), the probability of x, which should be a real number in the interval [0, 1].

Specified by:
prob in class DiscreteDistributionInt
Parameters:
x - value at which the mass function must be evaluated
Returns:
the mass function evaluated at x

cdf

public double cdf(int x)
Description copied from class: DiscreteDistributionInt
Returns the distribution function F evaluated at x (see).

Specified by:
cdf in class DiscreteDistributionInt
Parameters:
x - value at which the distribution function must be evaluated
Returns:
the distribution function evaluated at x

barF

public double barF(int x)
Description copied from class: DiscreteDistributionInt
Returns bar(F)(x), the complementary distribution function.

Overrides:
barF in class DiscreteDistributionInt
Parameters:
x - value at which the complementary distribution function must be evaluated
Returns:
the complementary distribution function evaluated at x

inverseFInt

public int inverseFInt(double u)
Description copied from class: DiscreteDistributionInt
Returns the inverse distribution function F-1(u), where 0 <= u <= 1. The default implementation uses binary search.

Overrides:
inverseFInt in class DiscreteDistributionInt
Parameters:
u - value in the interval (0, 1) for which the inverse distribution function is evaluated
Returns:
the inverse distribution function evaluated at u

getMean

public double getMean()
Description copied from interface: Distribution
Returns the mean of the distribution function.


getVariance

public double getVariance()
Description copied from interface: Distribution
Returns the variance of the distribution function.


getStandardDeviation

public double getStandardDeviation()
Description copied from interface: Distribution
Returns the standard deviation of the distribution function.


prob

public static double prob(double gamma,
                          double p,
                          int x)
Computes the probability mass function.


cdf

public static double cdf(double gamma,
                         double p,
                         int x)
Computes the distribution function.


inverseF

public static int inverseF(double gamma,
                           double p,
                           double u)
Computes the inverse function without precomputing tables. This method computes the CDF at the mode (maximum term) and performs a linear search from that point.


getInstanceFromMLE

public static NegativeBinomialDist getInstanceFromMLE(int[] x,
                                                      int n,
                                                      double gamma)
Creates a new instance of a negative binomial distribution with parameters γ = gamma given and hat(p) estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.

Parameters:
x - the list of observations to use to evaluate parameters
n - the number of observations to use to evaluate parameters
gamma - the first parameter of the negative binomial

getMaximumLikelihoodEstimate

public static double[] getMaximumLikelihoodEstimate(int[] x,
                                                    int n,
                                                    double gamma)
Estimates and returns the parameter [hat(p)] of the negative binomial distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1. The parameter γ = gamma is assumed known. The equation of maximum likelihood is defined as hat(p) = γ/(γ + bar(X)).

Parameters:
x - the list of observations used to evaluate parameters
n - the number of observations used to evaluate parameters
gamma - the first parameter of the negative binomial
Returns:
returns the parameters [hat(p)]

getInstanceFromMLE

public static NegativeBinomialDist getInstanceFromMLE(int[] x,
                                                      int n)
Creates a new instance of a negative binomial distribution with parameters γ and p estimated using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.

Parameters:
x - the list of observations to use to evaluate parameters
n - the number of observations to use to evaluate parameters

getMaximumLikelihoodEstimate

public static double[] getMaximumLikelihoodEstimate(int[] x,
                                                    int n)
Estimates and returns the parameters [ hat(γ), hat(p)] of the negative binomial distribution using the maximum likelihood method based on the n observations in table x[i], i = 0, 1,…, n - 1.

Parameters:
x - the list of observations used to evaluate parameters
n - the number of observations used to evaluate parameters
Returns:
returns the parameters [ hat(γ), hat(p)]

getMean

public static double getMean(double gamma,
                             double p)
Computes and returns the mean E[X] = γ(1 - p)/p of the negative binomial distribution with parameters γ and p.

Returns:
the mean of the negative binomial distribution E[X] = γ(1 - p)/p

getVariance

public static double getVariance(double gamma,
                                 double p)
Computes and returns the variance Var[X] = γ(1 - p)/p2 of the negative binomial distribution with parameters γ and p.

Returns:
the variance of the negative binomial distribution Var[X] = γ(1 - p)/p2

getStandardDeviation

public static double getStandardDeviation(double gamma,
                                          double p)
Computes and returns the standard deviation of the negative binomial distribution with parameters γ and p.

Returns:
the standard deviation of the negative binomial distribution

getGamma

public double getGamma()
Returns the parameter γ of this object.


getP

public double getP()
Returns the parameter p of this object.


setParams

public void setParams(double gamma,
                      double p)
Sets the parameter γ and p of this object.


SSJ
V. 1.2.5.

To submit a bug or ask questions, send an e-mail to Pierre L'Ecuyer.