probability distribution functions, such as exponential ones. In this paper we propose an approximation method, based on the Coxian distribution function. A Poisson random variable X with parameter µ has probability distribution . A random variable X has a Coxian distribution of order k if it has to go through up to . Evaluation of continuous phase–type distributions. . A discrete phase– type distribution is the distribution of the time to absorption in a.

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A phase-type distribution is a coxain distribution constructed by a convolution or mixture of exponential distributions. The sequence in which each of the phases occur may itself be a stochastic process.

The distribution can be represented by a random variable describing the time until absorption of a Markov process with one absorbing state. Each of the states of the Markov process represents one of the phases.

Phase-type distribution – Wikipedia

It has a discrete time equivalent the discrete phase-type distribution. The set of phase-type distributions is distrbiution in the field of all positive-valued distributions, that is, it coxkan be used to approximate any positive-valued distribution.

The continuous phase-type distribution is the distribution of time from the above process’s starting until absorption in the absorbing coxiann. This process can be written in the form of a transition rate matrix. It is usually assumed the probability of process starting in the absorbing state is zero i.

The moments of the distribution function are given by. The following probability distributions are all considered special cases of a continuous phase-type distribution:. As the phase-type distribution is dense in the field of all positive-valued distributions, we can represent any positive valued distribution.


However, the phase-type is a light-tailed or platykurtic distribution. So the representation of heavy-tailed or leptokurtic distribution by phase type is an approximation, even if the precision of the approximation can be as good as we want. The parameter of the phase-type distribution are: This mixture of densities of exponential distributed random variables can be characterized through.

For a given number of phases, the Erlang distribution is the phase type distribution with smallest coefficient of variation. The hypoexponential distribution is a generalisation of the Distributiom distribution by having different rates for each transition the non-homogeneous distributioh. The Coxian distribution is a generalisation of the hypoexponential dostribution. Instead of only being able to enter the absorbing state from state k it can be reached from any phase.

The phase-type representation is given by. The Coxian distribution is extremely important as any acyclic phase-type distribution has an equivalent Coxian representation.

CoxianDistribution—Wolfram Language Documentation

The generalised Coxian distribution relaxes the condition that requires starting in the first phase. Similarly to the exponential distributionthe class of PH distributions is closed under minima of independent random variables.

A description of this is here. BuTools includes methods for generating samples from phase-type distributed random variables. Any distribution can be arbitrarily well approximated by a phase type distribution.

Approximating a deterministic distribution of time 1 with 10 phases, each of average length 0.


Methods to fit a phase type distribution to data can be classified as maximum likelihood methods or moment matching methods. From Wikipedia, the free encyclopedia.


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Scandinavian Journal of Statistics. Benford Bernoulli beta-binomial binomial categorical hypergeometric Poisson binomial Rademacher soliton discrete uniform Zipf Zipf—Mandelbrot.

Cauchy exponential power Fisher’s z Gaussian q generalized normal generalized hyperbolic geometric stable Gumbel Holtsmark hyperbolic secant Johnson’s S U Landau Laplace asymmetric Laplace logistic noncentral t normal Gaussian normal-inverse Gaussian skew normal slash stable Student’s distfibution type-1 Gumbel Tracy—Widom variance-gamma Voigt. Discrete Ewens multinomial Dirichlet-multinomial negative multinomial Continuous Dirichlet generalized Dirichlet multivariate Laplace multivariate normal multivariate stable multivariate t normal-inverse-gamma normal-gamma Matrix-valued inverse matrix gamma inverse-Wishart matrix normal matrix t matrix gamma normal-inverse-Wishart normal-Wishart Wishart.

Degenerate Dirac delta function Singular Cantor. Circular compound Poisson elliptical exponential natural exponential location—scale maximum entropy mixture Pearson Tweedie wrapped. Retrieved from ” https: Continuous distributions Types of probability distributions. Views Read Edit View history. This page was last edited on 18 Octoberat By using this site, you agree to the Terms of Use and Privacy Policy.