Uses of Class
jline.lang.processes.ContinuousDistribution

Packages that use ContinuousDistribution

Package	Description
jline.lang.processes	This package contains processes and statistical distributions used to specify arrival rates, service rates, and item popularities

Uses of ContinuousDistribution in jline.lang.processes

Subclasses of ContinuousDistribution in jline.lang.processes

Modifier and Type	Class	Description
class	APH	An acyclic phase type distribution
class	BMAP	A Batch Markovian Arrival Process (BMAP) BMAP is a point process where arrivals occur in batches.
class	BMMAPt	BATCH, MARKED, time-inhomogeneous Markovian arrival process (BMMAP_t).
class	CME	A Concentrated Matrix Exponential (CME) distribution.
class	Cox2	A Coxian distribution with 2 phases.
class	Coxian	A general Coxian distribution with n phases.
class	Defective	A defective law: infinite with probability pInf, otherwise distributed as an inner continuous law G, so F(t) = (1-pInf) G(t) tends to 1-pInf < 1.
class	DMAP	A Discrete Markovian Arrival Process (DMAP).
class	Erlang	An Erlang-n distribution with n phases.
class	Exp	An exponential distribution.
class	Expolynomial	Expolynomial distribution with density f(x) = sum ci * x^ai * exp(-li*x).
class	Gamma	A Gamma distribution.
class	GMM	A Gaussian Mixture Model (GMM) distribution.
class	HyperExp	A hyper-exponential distribution.
class	LogLogistic	Log-logistic (Fisk) distribution with scale alpha (the median) and shape beta, F(t) = 1 / (1 + (t/alpha)^(-beta)) for t >= 0.
class	Lognormal	A Lognormal distribution.
class	MAP	A Markovian Arrival Process
class	MAPt	Time-inhomogeneous Markovian arrival process (MAP_t).
class	Marked	An abstract class for marked point processes
class	MarkedMAP	A Marked Markovian Arrival Process
class	MarkedMMPP	A Marked Markov-Modulated Poisson Process (M3PP)
class	Markovian	An abstract class for a Markovian distribution
class	MarkovModulated	An abstract class for a Markov-modulated point-process
class	MassAtOrigin	A law with an atom of probability p0 at zero, otherwise distributed as an inner continuous law G: F(t) = p0 + (1-p0) G(t), t >= 0.
class	ME	A Matrix Exponential (ME) distribution.
class	MMAP	A Marked Markovian Arrival Process (MMAP) MMAP is a point process where arrivals are categorized into K different types (marks).
class	MMAPt	Time-inhomogeneous MARKED Markovian arrival process (MMAP_t).
class	MMDP	A Markov-Modulated Deterministic Process (MMDP) for fluid queue modeling.
class	MMDP2	A 2-state Markov-Modulated Deterministic Process.
class	MMPP2	A Markovian-modulated Poisson Process with 2 states
class	MPH	A Marked Phase-Type distribution (MPH).
class	MPHt	Time-inhomogeneous MARKED phase-type distribution (MPH_t).
class	MultivariateNormal	A multivariate normal (Gaussian) distribution.
class	NHPP	A non-homogeneous Poisson process (NHPP) with a piecewise-constant intensity.
class	NNPP	A nonstationary non-Poisson arrival process (NNPP) generated by thinning.
class	Normal	A normal (Gaussian) distribution.
class	Pareto	A Pareto distribution
class	PH	A general phase-type (PH) distribution
class	PHt	Time-inhomogeneous phase-type distribution (Ph_t).
class	RAP	A Rational Arrival Process (RAP) distribution.
class	Uniform	A continuous Uniform distribution
class	Weibull	A Weibull distribution

Methods in jline.lang.processes with parameters of type ContinuousDistribution

Modifier and Type	Method	Description
static HyperExp	HyperExp.fit(ContinuousDistribution dist, String method)	Fit a hyperexponential to the ccdf of dist ITSELF at points spread over decades of time scale, rather than to its moments (HyperexpFitLongtail, Feldmann and Whitt 1998).