Uses of Class
jmt.common.exception.IncorrectDistributionParameterException

Packages that use IncorrectDistributionParameterException

Package	Description
jmt.jmch.distributions	Distribution support of the JMCH wizard.
NetStrategies.ForkStrategies	Strategies used by fork stations to split a job into the output tasks that are then routed to the downstream stations.
NetStrategies.RoutingStrategies	Routing strategies used by router nodes to select the destination station of each departing job.
QueueNet	Queueing network model of the JSIM simulation engine: nodes, stations, jobs, job classes, measures and the global structures that connect them.
random	Random number generation for the JSIM simulation engine.
random.discrete	Discrete probability distributions for the JSIM simulation engine (Bernoulli, binomial, geometric, Poisson, uniform, ...).

Uses of IncorrectDistributionParameterException in jmt.jmch.distributions

Methods in jmt.jmch.distributions that throw IncorrectDistributionParameterException

Modifier and Type	Method	Description
double	DistributionJMCH.nextRand()	Get the next random value for this distribution

Uses of IncorrectDistributionParameterException in NetStrategies.ForkStrategies

Methods in NetStrategies.ForkStrategies that throw IncorrectDistributionParameterException

Modifier and Type	Method	Description
void	OutPath.setJobsPerLinkDis(EmpiricalEntry[] jobsPerLinkDis)
void	OutPath.setProbabilityOfChosen(EmpiricalEntry probabilityOfChosen)

Constructors in NetStrategies.ForkStrategies that throw IncorrectDistributionParameterException

Modifier	Constructor	Description
	CombFork(EmpiricalEntry[] entries)
	OutPath(EmpiricalEntry probabilityOfChosen, EmpiricalEntry[] jobsPerLinkDis)
	ProbabilitiesFork(OutPath[] outPaths)

Uses of IncorrectDistributionParameterException in NetStrategies.RoutingStrategies

Constructors in NetStrategies.RoutingStrategies that throw IncorrectDistributionParameterException

Modifier	Constructor	Description
	ClassSwitchRoutingParameter(String destNode, Double destProbability, EmpiricalEntry[] entries)
	ClassSwitchRoutingStrategy(ClassSwitchRoutingParameter[] entries)
	EmpiricalStrategy(EmpiricalEntry[] entries)	Creates an empirical strategy using the passed data.
	LoadDependentRoutingParameter(Integer from, EmpiricalEntry[] entires)

Uses of IncorrectDistributionParameterException in QueueNet

Methods in QueueNet that throw IncorrectDistributionParameterException

Modifier and Type	Method	Description
protected abstract int	NodeSection.process(NetMessage message)	This method should be overridden to implement a specific NetMessage processor.

Uses of IncorrectDistributionParameterException in random

Methods in random that throw IncorrectDistributionParameterException

Modifier and Type	Method	Description
double	Batch.cdf(double x, Parameter p)
double	Burst.cdf(double x, Parameter p)
double	CoxianDistr.cdf(double x, Parameter p)	This method is used to obtain from the distribution its cumulative distribution function evaluated where required by the user.
double	DeterministicDistr.cdf(double x, Parameter p)	This method is used to obtain from the distribution its cumulative distribution function evaluated where required by the user.
double	Distribution.cdf(double x, Parameter p)	This method is used to obtain from the distribution its cumulative distribution function evaluated where required by the user.
double	Empirical.cdf(double x, Parameter p)	This method is used to obtain from the distribution its cumulative distribution function evaluated where required by the user.
double	Erlang.cdf(double x, Parameter p)	This method is used to obtain from the distribution its cumulative distribution function evaluated where required by the user.
double	Exponential.cdf(double x, Parameter p)	This method is used to obtain from the distribution its cumulative distribution function evaluated where required by the user.
double	GammaDistr.cdf(double x, Parameter p)	This method is used to obtain from the distribution its cumulative distribution function evaluated where required by the user.
double	HyperExp.cdf(double x, Parameter p)	This method is used to obtain from the distribution its cumulative distribution function evaluated where required by the user.
double	Lognormal.cdf(double x, Parameter p)	This method is used to obtain from the distribution its cumulative distribution function evaluated where required by the user.
double	MAPDistr.cdf(double x, Parameter p)	This method is used to obtain from the distribution its cumulative distribution function evaluated where required by the user.
double	MMPP2Distr.cdf(double x, Parameter p)	This method is used to obtain from the distribution its cumulative distribution function evaluated where required by the user.
double	Normal.cdf(double x, Parameter p)	This method is used to obtain from the distribution its cumulative distribution function evaluated where required by the user.
double	Pareto.cdf(double x, Parameter p)	This method is used to obtain from the distribution its cumulative distribution function evaluated where required by the user.
double	PhaseTypeDistr.cdf(double x, Parameter p)	This method is used to obtain from the distribution its cumulative distribution function evaluated where required by the user.
double	Poisson.cdf(double x, Parameter p)	This method is used to obtain from the distribution its cumulative distribution function evaluated where required by the user.
double	Replayer.cdf(double x, Parameter p)	This method is used to obtain from the distribution its cumulative distribution function evaluated where required by the user.
double	StudentT.cdf(double x, Parameter p)	This method is used to obtain from the distribution its cumulative distribution function evaluated where required by the user.
double	Uniform.cdf(double x, Parameter p)	This method is used to obtain from the distribution its cumulative distribution function evaluated where required by the user.
double	Weibull.cdf(double x, Parameter p)	This method is used to obtain from the distribution its cumulative distribution function evaluated where required by the user.
void	Burst.initialize()
int	Uniform.nextInt(Parameter p)	This method is used to obtain from the distribution the next integer number distributed according to the distribution parameter.
double	Uniform.nextLong(Parameter p)	This method is used to obtain from the distribution the next Long integer number distributed according to the distribution parameter.
double	Batch.nextRand(Parameter p)	This method is used to obtain from the distribution the next number distributed according to the distribution parameter.
double	Burst.nextRand(Parameter p)	This method is used to obtain from the distribution the next number distributed according to the distribution parameter and the actual interval (A or B).
double	CoxianDistr.nextRand(Parameter p)	This method is used to obtain from the distribution the next number distributed according to the distribution parameter.
double	DeterministicDistr.nextRand(Parameter p)	This method is used to obtain from the distribution the next number distributed according to the distribution parameter.
double	Distribution.nextRand(Parameter p)	This method is used to ask all the distribution to return a new random number distributed according to the distribution name.
double	Empirical.nextRand(Parameter p)	This method is used to obtain from the distribution the next number distributed according to the distribution parameter.
double	Erlang.nextRand(Parameter p)	This method is used to obtain from the distribution the next number distributed according to the distribution parameter.
double	Exponential.nextRand(Parameter p)	This method is used to obtain from the distribution the next number distributed according to the distribution parameter.
double	GammaDistr.nextRand(Parameter p)	This method is used to obtain from the distribution the next number distributed according to the distribution parameter.
double	HyperExp.nextRand(Parameter p)	This method is used to obtain from the distribution the next number distributed according to the distribution parameter.
double	Lognormal.nextRand(Parameter p)	This method is used to obtain from the distribution the next number distributed according to the distribution parameter.
double	MAPDistr.nextRand(Parameter p)	This method is used to obtain from the distribution the next number distributed according to the distribution parameter.
double	MMPP2Distr.nextRand(Parameter p)	This method is used to obtain from the distribution the next number distributed according to the distribution parameter.
double	Normal.nextRand(Parameter p)	This method is used to obtain from the distribution the next number distributed according to the distribution parameter.
double	Pareto.nextRand(Parameter p)	This method is used to obtain from the distribution the next number distributed according to the distribution parameter.
double	PhaseTypeDistr.nextRand(Parameter p)	This method is used to obtain from the distribution the next number distributed according to the distribution parameter.
double	Poisson.nextRand(Parameter p)	This method is used to obtain from the distribution the next number distributed according to the distribution parameter.
double	Replayer.nextRand(Parameter p)	Reads a new data from the file.
double	StudentT.nextRand(Parameter p)	This method is used to obtain from the distribution the next number distributed according to the distribution parameter.
double	Uniform.nextRand(Parameter p)	This method is used to obtain from the distribution the next number distributed according to the distribution parameter.
double	Weibull.nextRand(Parameter p)	This method is used to obtain from the distribution the next number distributed according to the distribution parameter.
double	Batch.pdf(double x, Parameter p)
double	Burst.pdf(double x, Parameter p)
double	CoxianDistr.pdf(double x, Parameter p)	This method is used to obtain from the distribution its probability distribution function evaluated where required by the user.
double	DeterministicDistr.pdf(double x, Parameter p)	This method is used to obtain from the distribution its probability distribution function evaluated where required by the user.
double	Distribution.pdf(double x, Parameter p)	This method is used to obtain from the distribution its probability distribution function evaluated where required by the user.
double	Empirical.pdf(double x, Parameter p)	This method is used to obtain from the distribution its probability distribution function evaluated where required by the user.
double	Erlang.pdf(double x, Parameter p)	This method is used to obtain from the distribution its probability distribution function evaluated where required by the user.
double	Exponential.pdf(double x, Parameter p)	This method is used to obtain from the distribution its probability distribution function evaluated where required by the user.
double	GammaDistr.pdf(double x, Parameter p)	This method is used to obtain from the distribution its probability distribution function evaluated where required by the user.
double	HyperExp.pdf(double x, Parameter p)	This method is used to obtain from the distribution its probability distribution function evaluated where required by the user.
double	Lognormal.pdf(double x, Parameter p)	This method is used to obtain from the distribution its probability distribution function evaluated where required by the user.
double	MAPDistr.pdf(double x, Parameter p)	This method is used to obtain from the distribution its probability distribution function evaluated where required by the user.
double	MMPP2Distr.pdf(double x, Parameter p)	This method is used to obtain from the distribution its probability distribution function evaluated where required by the user.
double	Normal.pdf(double x, Parameter p)	This method is used to obtain from the distribution its probability distribution function evaluated where required by the user.
double	Pareto.pdf(double x, Parameter p)	This method is used to obtain from the distribution its probability distribution function evaluated where required by the user.
double	PhaseTypeDistr.pdf(double x, Parameter p)	This method is used to obtain from the distribution its probability distribution function evaluated where required by the user.
double	Poisson.pdf(double x, Parameter p)	This method is used to obtain from the distribution its probability distribution function evaluated where required by the user.
double	Replayer.pdf(double x, Parameter p)	This method is used to obtain from the distribution its probability distribution function evaluated where required by the user.
double	StudentT.pdf(double x, Parameter p)	This method is used to obtain from the distribution its probability distribution function evaluated where required by the user.
double	Uniform.pdf(double x, Parameter p)	This method is used to obtain from the distribution its probability distribution function evaluated where required by the user.
double	Weibull.pdf(double x, Parameter p)	This method is used to obtain from the distribution its probability distribution function evaluated where required by the user.
void	ErlangPar.setAlpha(double alpha)	It allows the user to modify the value of the parameter alpha of the erlang distribution.
void	GammaDistrPar.setAlpha(double alpha)	It allows the user to modify the value of the parameter alpha of the gamma distribution.
void	ParetoPar.setAlpha(double alpha)	It allows the user to change the value of the parameter alpha of the pareto distribution and verifies if alpha is greater than two.
void	WeibullPar.setAlpha(double alpha)	It allows the user to modify the value of the parameter alpha of the erlang distribution.
void	StudentTPar.setFreedom(double freedom)	It allows the user to change the degrees of freedom of the student T distribution and verifies if it is an integer greater than zero.
void	ParetoPar.setK(double k)	It allows the user to change the value of the parameter k of the pareto distribution.
void	ExponentialPar.setLambda(double lambda)	It allows the user to modify the value of the parameter lambda of the exponential distribution and verify if the value is correct that is if it is greater than zero.
void	GammaDistrPar.setLambda(double lambda)	It allows the user to modify the value of the parameter lambda of the gamma distribution.
void	PoissonPar.setLambda(double lambda)	It allows the user to modify the value of the parameter lambda of the Poisson distribution and verify if the value is correct that is if it is greater than zero.
void	UniformPar.setMax(double max)	It allows the user to change the maximum bound of the uniform distribution and verifies if it is correct.
void	AbstractParameter.setMean(double value)	Sets mean for a given distribution parameter.
void	CoxianPar.setMean(double value)	Sets mean for a given distribution parameter.
void	DeterministicDistrPar.setMean(double value)	Sets mean for a given distribution parameter.
void	ErlangPar.setMean(double value)	Sets mean for a given distribution parameter.
void	ExponentialPar.setMean(double value)	Sets mean for a given distribution parameter.
void	GammaDistrPar.setMean(double value)	Sets mean for a given distribution parameter.
void	HyperExpPar.setMean(double value)	Sets mean for a given distribution parameter.
void	NormalPar.setMean(double mean)	It allows the user to change the value of the mean of the normal distribution.
void	Parameter.setMean(double value)	Sets mean for a given distribution parameter.
void	ParetoPar.setMean(double value)	Sets mean for a given distribution parameter.
void	PoissonPar.setMean(double value)	Sets mean for a given distribution parameter.
void	UniformPar.setMean(double value)	Sets mean for a given distribution parameter.
void	WeibullPar.setMean(double value)	Sets mean for a given distribution parameter.
void	UniformPar.setMin(double min)	It allows the user to change the minimum bound of the uniform distribution and verifies if it is correct.
void	LognormalPar.setMu(double mu)	It allows the user to change the value of the mean of the normal distribution.
void	DeterministicDistrPar.setParameterValue(double t)	It allows the user to change the value of the parameter of the deterministic distribution.
void	EmpiricalPar.setPDF(double[] pdf)	It allows the user to change the value of the parameter of the empirical distribution.
void	ErlangPar.setR(long r)	It allows the user to modify the value of the parameter r of the erlang distribution.
void	WeibullPar.setR(double r)	It allows the user to modify the value of the parameter r of the erlang distribution.
void	LognormalPar.setSigma(double sigma)	It allows the user to change the value of the standard deviation of the normal distribution and verifies if it is greater than zero.
void	NormalPar.setStandardDeviation(double standardDeviation)	It allows the user to change the value of the standard deviation of the normal distribution and verifies if it is greater than zero.
double	Batch.theorMean(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic mean.
double	Burst.theorMean(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic mean.
double	CoxianDistr.theorMean(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic mean.
double	DeterministicDistr.theorMean(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic mean.
double	Distribution.theorMean(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic mean.
double	Empirical.theorMean(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic mean, which is is calculated from the data provided by the user (contained in the parameter p) as the sum of all the data value, divided by the number of data.
double	Erlang.theorMean(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic mean.
double	Exponential.theorMean(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic mean.
double	GammaDistr.theorMean(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic mean.
double	HyperExp.theorMean(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic mean.
double	Lognormal.theorMean(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic mean.
double	MAPDistr.theorMean(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic mean.
double	MMPP2Distr.theorMean(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic mean.
double	Normal.theorMean(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic mean.
double	Pareto.theorMean(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic mean.
double	PhaseTypeDistr.theorMean(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic mean.
double	Poisson.theorMean(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic mean.
double	Replayer.theorMean(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic mean.
double	StudentT.theorMean(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic mean.
double	Uniform.theorMean(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic mean.
double	Weibull.theorMean(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic mean.
double	Batch.theorVariance(Parameter p)
double	Burst.theorVariance(Parameter p)
double	CoxianDistr.theorVariance(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic variance.
double	DeterministicDistr.theorVariance(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic variance.
double	Distribution.theorVariance(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic variance.
double	Empirical.theorVariance(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic variance which is calculated from the data provided by the user (contained in the parameter p) as the sum of all the squares of the differences between each data value and the theoretic mean, divided by the number of data.
double	Erlang.theorVariance(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic variance.
double	Exponential.theorVariance(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic variance.
double	GammaDistr.theorVariance(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic variance.
double	HyperExp.theorVariance(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic variance.
double	Lognormal.theorVariance(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic variance.
double	MAPDistr.theorVariance(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic variance.
double	MMPP2Distr.theorVariance(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic variance.
double	Normal.theorVariance(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic variance.
double	Pareto.theorVariance(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic variance.
double	PhaseTypeDistr.theorVariance(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic variance.
double	Poisson.theorVariance(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic variance.
double	Replayer.theorVariance(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic variance.
double	StudentT.theorVariance(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic variance.
double	Uniform.theorVariance(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic variance.
double	Weibull.theorVariance(Parameter p)	This method is used to obtain from the distribution the value of its own theoretic variance.

Constructors in random that throw IncorrectDistributionParameterException

Modifier	Constructor	Description
	BatchPar(DistributionContainer base, DistributionContainer batchSize, Integer fixedBatchSize, Boolean IsFixedBatchSize)	It creates a new batched parameter.
	Burst(Double probability, DistributionContainer distrContA, DistributionContainer distrContB)	Creates a new non-Round-Robin Burst distribution with the specified probability of having events of type A.
	Burst(Double probability, DistributionContainer distrContA, DistributionContainer distrContB, Boolean isRoundRobin)	Creates a new Burst distribution with the specified probability of having events of type A.
	CoxianPar(double lambda0, double lambda1, double phi0)	It creates a new coxian parameter based on the data provided by the user.
	CoxianPar(Double wlambda0, Double wlambda1, Double wphi0)
	DeterministicDistrPar(double t)	It creates a new deterministic parameter.
	DeterministicDistrPar(Double t)	It creates a new deterministic parameter.
	EmpiricalPar(double[] pdf)	It creates a new empirical parameter.
	EmpiricalPar(Double[] wpdf)	It creates a new empirical parameter.
	EmpiricalPar(EmpiricalEntry[] entries)	Creates an empirical parameter with these entries (each entry contains a value and a probability).
	ErlangPar(double alpha, double r)
	ErlangPar(double alpha, long r)	It creates a new erlang parameter with the information provided by the user.
	ErlangPar(Double walpha, Double wr)
	ErlangPar(Double walpha, Long wr)
	ExponentialPar(double lambda)	It creates a new exponential parameter using the value provided by the user.
	ExponentialPar(Double wlambda)
	GammaDistrPar(double alpha, double lambda)	It creates a new gamma parameter according to the value provided by the user.
	GammaDistrPar(Double walpha, Double wlambda)
	HyperExpPar(double p, double lambda1, double lambda2)	It creates a new hyperexponential parameter based on the data provided by the user.
	HyperExpPar(Double wp, Double wlambda1, Double wlambda2)
	LognormalPar(double mu, double sigma)	It creates a new normal parameter according to the data provided by the user: the mean and the standard deviation of the normal distribution.
	LognormalPar(Double wmu, Double wsigma)
	MAPPar(Object[] D0, Object[] D1)	It creates a new MAP parameter based on the data provided by the user.
	MMPP2Par(double lambda0, double lambda1, double sigma0, double sigma1)	It creates a new MMPP2 parameter based on the data provided by the user.
	MMPP2Par(Double wlambda0, Double wlambda1, Double wsigma0, Double wsigma1)
	NormalPar(double mean, double standardDeviation)	It creates a new normal parameter according to the data provided by the user: the mean and the standard deviation of the normal distribution.
	NormalPar(Double wmean, Double wstandardDeviation)
	ParetoPar(double alpha, double k)	It creates a new pareto parameter according to the data provided by the user: the parameter alpha and k of the pareto distribution.
	ParetoPar(Double walpha, Double wk)
	PhaseTypePar(Object[] alpha, Object[] T)	It creates a new phase-type parameter based on the data provided by the user.
	PoissonPar(double lambda)	It creates a new Poisson parameter with the mean provided by the user.
	PoissonPar(Double wmean)
	StudentTPar(double freedom)	It creates a new student T parameter with the provided degrees of freedom.
	StudentTPar(Double wfreedom)
	UniformPar(double min, double max)	It creates a new uniform parameter using the bounds provided by the user.
	UniformPar(Double wmin, Double wmax)
	WeibullPar(double alpha, double r)	It creates a new Weibull parameter with the information provided by the user.
	WeibullPar(Double walpha, Double wr)

Uses of IncorrectDistributionParameterException in random.discrete

Methods in random.discrete that throw IncorrectDistributionParameterException

Modifier and Type	Method	Description
double	Bernoulli.cdf(int x, Parameter p)
double	Binomial.cdf(int x, Parameter par)
abstract double	DiscreteDistribution.cdf(int x, Parameter p)	obtain the cumulative distribution from the distribution
double	FixProbability.cdf(int x, Parameter p)
double	Poisson.cdf(int x, Parameter p)
double	ShiftedGeometric.cdf(int x, Parameter p)
double	Uniform.cdf(int x, Parameter p)
double	Zipf.cdf(int x, Parameter p)
protected Hashtable<Integer,Double>	DiscreteDistribution.createdCDFList(int lower, int upper, Parameter p)
int	Bernoulli.nextRand(Parameter p)
int	Binomial.nextRand(Parameter par)
abstract int	DiscreteDistribution.nextRand(Parameter p)	Generate random variables that follow an probability distribution
int	FixProbability.nextRand(Parameter p)
int	Poisson.nextRand(Parameter p)
int	ShiftedGeometric.nextRand(Parameter p)
int	Uniform.nextRand(Parameter p)
int	Zipf.nextRand(Parameter p)
double	Bernoulli.pmf(int x, Parameter p)
double	Binomial.pmf(int x, Parameter par)
abstract double	DiscreteDistribution.pmf(int x, Parameter p)	obtain the probability distribution from the distribution
double	FixProbability.pmf(int x, Parameter p)
double	Poisson.pmf(int x, Parameter p)
double	ShiftedGeometric.pmf(int x, Parameter p)
double	Uniform.pmf(int x, Parameter p)
double	Zipf.pmf(int x, Parameter p)
void	ZipfPar.setAlpha(double alpha)
void	PoissonPar.setLambda(double lambda)
void	UniformPar.setMax(int b)
void	ZipfPar.setMax(int numberOfElements)
void	BernoulliPar.setMean(double value)
void	PoissonPar.setMean(double value)
void	ShiftedGeometricPar.setMean(double value)
void	UniformPar.setMin(int a)
void	BinomialPar.setNumberOfExperiment(int n)
void	BernoulliPar.setProbability(double p)
void	BinomialPar.setProbability(double probability)
void	ShiftedGeometricPar.setProbability(double p)
double	Bernoulli.theorMean(Parameter p)
double	Binomial.theorMean(Parameter par)
abstract double	DiscreteDistribution.theorMean(Parameter p)	Calculate the Mean value from the parameter of the distribution
double	FixProbability.theorMean(Parameter p)
double	Poisson.theorMean(Parameter p)
double	ShiftedGeometric.theorMean(Parameter p)
double	Uniform.theorMean(Parameter p)
double	Zipf.theorMean(Parameter p)
double	Bernoulli.theorVariance(Parameter p)
double	Binomial.theorVariance(Parameter par)
abstract double	DiscreteDistribution.theorVariance(Parameter p)	Calculate the Variance from the parameter of the distribution
double	FixProbability.theorVariance(Parameter p)
double	Poisson.theorVariance(Parameter p)
double	ShiftedGeometric.theorVariance(Parameter p)
double	Uniform.theorVariance(Parameter p)
double	Zipf.theorVariance(Parameter p)
protected <T> T	Poisson.unboxParameter(Parameter p, Function<Double,T> function)
protected <T> T	ShiftedGeometric.unboxParameter(Parameter p, Function<Double,T> function)
boolean	Bernoulli.updatePar(double p)
boolean	Bernoulli.updatePar(Parameter p)
boolean	Binomial.updatePar(double p, int n)
boolean	Binomial.updatePar(Parameter p)
abstract boolean	DiscreteDistribution.updatePar(Parameter p)	Sub-class must implement this method to update parameter.
boolean	FixProbability.updatePar(Parameter p)
boolean	Poisson.updatePar(double lambda)
boolean	Poisson.updatePar(Parameter p)
boolean	ShiftedGeometric.updatePar(double p)
boolean	ShiftedGeometric.updatePar(Parameter p)
boolean	Uniform.updatePar(int a, int b)
boolean	Uniform.updatePar(Parameter p)
boolean	Zipf.updatePar(double alpha, int numberOfElements)
boolean	Zipf.updatePar(Parameter p)

Constructors in random.discrete that throw IncorrectDistributionParameterException

Modifier	Constructor	Description
	Bernoulli(double p)
	Bernoulli(Double p)
	BernoulliPar(double p)
	BernoulliPar(Double p)
	Binomial(double p, int n)
	Binomial(Double p, Integer numberOfItems)
	Binomial(Integer numberOfItems, Double p)
	BinomialPar(int n, double probability)
	BinomialPar(Double probability, Integer numberOfItems)
	BinomialPar(Integer numberOfItems, Double probability)
	Poisson(double lambda)
	Poisson(Double lambda)
	PoissonPar(double lambda)
	PoissonPar(Double lambda)
	ShiftedGeometric(double p)
	ShiftedGeometric(Double p)
	ShiftedGeometricPar(double p)
	ShiftedGeometricPar(Double p)
	Uniform(int a, int b)
	Uniform(Integer a, Integer b)
	UniformPar(int a, int b)
	UniformPar(Integer a, Integer b)
	Zipf(double alpha, int numberOfElements)
	Zipf(Double alpha, Integer numberOfElements)
	Zipf(Integer numberOfElements, Double alpha)
	ZipfPar(int numberOfElements, double alpha)
	ZipfPar(Double alpha, Integer numberOfElements)
	ZipfPar(Integer numberOfElements, Double alpha)