Documentation API of the 'jhplot.fit.NegativeBinomial' Java class
NegativeBinomial
jhplot.fit

Class NegativeBinomial

  • All Implemented Interfaces:
    IDevManagedObject, IFunction, IManagedObject, IModelFunction, Connectable, FunctionDispatcher, Cloneable


    public class NegativeBinomialextends AbstractIFunction
    Negative binomial distribution. Returns the sum of the terms 0 through k of the Negative Binomial Distribution.
       k   --  ( n+j-1 )   n      j   >   (       )  p  (1-p)   --  (   j   )  j=0 
    In a sequence of Bernoulli trials, this is the probability that k or fewer failures precede the n-th success.

    The terms are not computed individually; instead the incomplete beta integral is employed, according to the formula

    y = negativeBinomial( k, n, p ) = Gamma.incompleteBeta( n, k+1, p ). All arguments must be positive, p[0] - scale factor
    p[1] - the number of trials.
    p[2] - the probability of success (must be in (0.0,1.0)).

    • Constructor Detail

      • NegativeBinomial

        public NegativeBinomial()
      • NegativeBinomial

        public NegativeBinomial(String title)
      • NegativeBinomial

        public NegativeBinomial(String[] variableNames,                        String[] parameterNames)

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NegativeBinomial
jhplot.fit

Class NegativeBinomial

  • All Implemented Interfaces:
    IDevManagedObject, IFunction, IManagedObject, IModelFunction, Connectable, FunctionDispatcher, Cloneable


    public class NegativeBinomialextends AbstractIFunction
    Negative binomial distribution. Returns the sum of the terms 0 through k of the Negative Binomial Distribution.
       k   --  ( n+j-1 )   n      j   >   (       )  p  (1-p)   --  (   j   )  j=0 
    In a sequence of Bernoulli trials, this is the probability that k or fewer failures precede the n-th success.

    The terms are not computed individually; instead the incomplete beta integral is employed, according to the formula

    y = negativeBinomial( k, n, p ) = Gamma.incompleteBeta( n, k+1, p ). All arguments must be positive, p[0] - scale factor
    p[1] - the number of trials.
    p[2] - the probability of success (must be in (0.0,1.0)).

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