Phase-type distribution

Phase-type
Parameters	$S,\;m\times m$ subgenerator matrix ${\boldsymbol {\alpha }}$ , probability row vector
Support	$x\in [0;\infty )\!$
PDF	${\boldsymbol {\alpha }}e^{{xS}}{\boldsymbol {S}}^{{0}}$ See article for details
CDF	$1-{\boldsymbol {\alpha }}e^{{xS}}{\boldsymbol {1}}$
Mean	$-{\boldsymbol {\alpha }}{S}^{{-1}}{\mathbf {1}}$
Median	no simple closed form
Mode	no simple closed form
Variance	$2{\boldsymbol {\alpha }}{S}^{{-2}}{\mathbf {1}}-({\boldsymbol {\alpha }}{S}^{{-1}}{\mathbf {1}})^{{2}}$
MGF	$-{\boldsymbol {\alpha }}(tI+S)^{{-1}}{\boldsymbol {S}}^{{0}}+\alpha _{{0}}$
CF	$-{\boldsymbol {\alpha }}(itI+S)^{{-1}}{\boldsymbol {S}}^{{0}}+\alpha _{{0}}$

A phase-type distribution is a probability distribution constructed by a convolution or mixture of exponential distributions.^[1] It results from a system of one or more inter-related Poisson processes occurring in sequence, or phases. 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.

It has a discrete time equivalent the discrete phase-type distribution.

The set of phase-type distributions is dense in the field of all positive-valued distributions, that is, it can be used to approximate any positive-valued distribution.

Definition

Consider a continuous-time Markov process with m + 1 states, where m ≥ 1, such that the states 1,...,m are transient states and state 0 is an absorbing state. Further, let the process have an initial probability of starting in any of the m + 1 phases given by the probability vector (α₀,α) where α₀ is a scalar and α is a 1 × m vector.

The continuous phase-type distribution is the distribution of time from the above process's starting until absorption in the absorbing state.

This process can be written in the form of a transition rate matrix,

{Q}=\left[{\begin{matrix}0&{\mathbf {0}}\\{\mathbf {S}}^{0}&{S}\\\end{matrix}}\right],

where S is an m × m matrix and S⁰ = –S1. Here 1 represents an m × 1 vector with every element being 1.

Characterization

The distribution of time X until the process reaches the absorbing state is said to be phase-type distributed and is denoted PH(α,S).

The distribution function of X is given by,

F(x)=1-{\boldsymbol {\alpha }}\exp({S}x){\mathbf {1}},

and the density function,

f(x)={\boldsymbol {\alpha }}\exp({S}x){\mathbf {S^{{0}}}},

for all x > 0, where exp( · ) is the matrix exponential. It is usually assumed the probability of process starting in the absorbing state is zero (i.e. α₀= 0). The moments of the distribution function are given by

E[X^{{n}}]=(-1)^{{n}}n!{\boldsymbol {\alpha }}{S}^{{-n}}{\mathbf {1}}.

The Laplace transform of the phase type distribution is given by

M(s)=\alpha _{0}+{\boldsymbol {\alpha }}(sI-S)^{-1}\mathbf {S^{0}} ,

where I is the identity matrix.

Special cases

The following probability distributions are all considered special cases of a continuous phase-type distribution:

Degenerate distribution, point mass at zero or the empty phase-type distribution - 0 phases.
Exponential distribution - 1 phase.
Erlang distribution - 2 or more identical phases in sequence.
Deterministic distribution (or constant) - The limiting case of an Erlang distribution, as the number of phases become infinite, while the time in each state becomes zero.
Coxian distribution - 2 or more (not necessarily identical) phases in sequence, with a probability of transitioning to the terminating/absorbing state after each phase.
Hyper-exponential distribution (also called a mixture of exponential) - 2 or more non-identical phases, that each have a probability of occurring in a mutually exclusive, or parallel, manner. (Note: The exponential distribution is the degenerate situation when all the parallel phases are identical.)
Hypoexponential distribution - 2 or more phases in sequence, can be non-identical or a mixture of identical and non-identical phases, generalises the Erlang.

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 platikurtic 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.

Examples

In all the following examples it is assumed that there is no probability mass at zero, that is α₀ = 0.

Exponential distribution

The simplest non-trivial example of a phase-type distribution is the exponential distribution of parameter λ. The parameter of the phase-type distribution are : S = -λ and α = 1.

Hyper-exponential or mixture of exponential distribution

The mixture of exponential or hyper-exponential distribution with λ₁,λ₂,...,λ_n>0 can be represented as a phase type distribution with

{\boldsymbol {\alpha }}=(\alpha _{1},\alpha _{2},\alpha _{3},\alpha _{4},...,\alpha _{n})

with $\sum _{{i=1}}^{n}\alpha _{i}=1$ and

{S}=\left[{\begin{matrix}-\lambda _{1}&0&0&0&0\\0&-\lambda _{2}&0&0&0\\0&0&-\lambda _{3}&0&0\\0&0&0&-\lambda _{4}&0\\0&0&0&0&-\lambda _{5}\\\end{matrix}}\right].

This mixture of densities of exponential distributed random variables can be characterized through

f(x)=\sum _{{i=1}}^{n}\alpha _{i}\lambda _{i}e^{{-\lambda _{i}x}}=\sum _{{i=1}}^{n}\alpha _{i}f_{{X_{i}}}(x),

or its cumulative distribution function

F(x)=1-\sum _{{i=1}}^{n}\alpha _{i}e^{{-\lambda _{i}x}}=\sum _{{i=1}}^{n}\alpha _{i}F_{{X_{i}}}(x).

with $X_{i}\sim Exp(\lambda _{i})$

Erlang distribution

The Erlang distribution has two parameters, the shape an integer k > 0 and the rate λ > 0. This is sometimes denoted E(k,λ). The Erlang distribution can be written in the form of a phase-type distribution by making S a k×k matrix with diagonal elements -λ and super-diagonal elements λ, with the probability of starting in state 1 equal to 1. For example, E(5,λ),

{\boldsymbol {\alpha }}=(1,0,0,0,0),

and

{S}=\left[{\begin{matrix}-\lambda &\lambda &0&0&0\\0&-\lambda &\lambda &0&0\\0&0&-\lambda &\lambda &0\\0&0&0&-\lambda &\lambda \\0&0&0&0&-\lambda \\\end{matrix}}\right].

For a given number of phases, the Erlang distribution is the phase type distribution with smallest coefficient of variation.^[2]

The hypoexponential distribution is a generalisation of the Erlang distribution by having different rates for each transition (the non-homogeneous case).

Mixture of Erlang distribution

The mixture of two Erlang distribution with parameter E(3,β₁), E(3,β₂) and (α₁,α₂) (such that α₁ + α₂ = 1 and for each i, α_i ≥ 0) can be represented as a phase type distribution with

{\boldsymbol {\alpha }}=(\alpha _{1},0,0,\alpha _{2},0,0),

and

{S}=\left[{\begin{matrix}-\beta _{1}&\beta _{1}&0&0&0&0\\0&-\beta _{1}&\beta _{1}&0&0&0\\0&0&-\beta _{1}&0&0&0\\0&0&0&-\beta _{2}&\beta _{2}&0\\0&0&0&0&-\beta _{2}&\beta _{2}\\0&0&0&0&0&-\beta _{2}\\\end{matrix}}\right].

Coxian distribution

The Coxian distribution is a generalisation of the hypoexponential distribution. 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,

S=\left[{\begin{matrix}-\lambda _{{1}}&p_{{1}}\lambda _{{1}}&0&\dots &0&0\\0&-\lambda _{{2}}&p_{{2}}\lambda _{{2}}&\ddots &0&0\\\vdots &\ddots &\ddots &\ddots &\ddots &\vdots \\0&0&\ddots &-\lambda _{{k-2}}&p_{{k-2}}\lambda _{{k-2}}&0\\0&0&\dots &0&-\lambda _{{k-1}}&p_{{k-1}}\lambda _{{k-1}}\\0&0&\dots &0&0&-\lambda _{{k}}\end{matrix}}\right]

and

{\boldsymbol {\alpha }}=(1,0,\dots ,0),

where 0 < p₁,...,p_k-1 ≤ 1. In the case where all p_i = 1 we have the hypoexponential distribution. The Coxian distribution is extremely important as any acyclic phase-type distribution has an equivalent Coxian representation.

The generalised Coxian distribution relaxes the condition that requires starting in the first phase.

Properties

Minima of Independent PH Random Variables

Similarly to the exponential distribution, the class of PH distributions is closed under minima of independent random variables. A description of this is here.

Generating samples from phase-type distributed random variables

BuTools includes methods for generating samples from phase-type distributed random variables.^[3]

Approximating other distributions

Any distribution can be arbitrarily well approximated by a phase type distribution.^[4]^[5] In practice, however, approximations can be poor when the size of the approximating process is fixed. Approximating a deterministic distribution of time 1 with 10 phases, each of average length 0.1 will have variance 0.1 (because the Erlang distribution has smallest variance^[2]).

BuTools a MATLAB and Mathematica script for fitting phase-type distributions to 3 specified moments
momentmatching a MATLAB script to fit a minimal phase-type distribution to 3 specified moments^[6]

Fitting a phase type distribution to data

Methods to fit a phase type distribution to data can be classified as maximum likelihood methods or moment matching methods.^[7] Fitting a phase type distribution to heavy-tailed distributions has been shown to be practical in some situations.^[8]

PhFit a C script for fitting discrete and continuous phase type distributions to data^[9]
EMpht is a C script for fitting phase-type distributions to data or parametric distributions using an expectation–maximization algorithm.^[10]
HyperStar was developed around the core idea of making phase-type fitting simple and user-friendly, in order to advance the use of phase-type distributions in a wide range of areas. It provides a graphical user interface and yields good fitting results with only little user interaction.^[11]
jPhase is a Java library which can also compute metrics for queues using the fitted phase type distribution^[12]

References

↑ Harchol-Balter, M. (2012). "Real-World Workloads: High Variability and Heavy Tails". Performance Modeling and Design of Computer Systems. p. 347. ISBN 9781139226424. doi:10.1017/CBO9781139226424.026.
1 2 Aldous, David; Shepp, Larry (1987). "The least variable phase type distribution is erlang" (PDF). Stochastic Models. 3 (3): 467. doi:10.1080/15326348708807067.
↑ Horváth, G. B.; Reinecke, P.; Telek, M. S.; Wolter, K. (2012). "Efficient Generation of PH-Distributed Random Variates". Analytical and Stochastic Modeling Techniques and Applications. Lecture Notes in Computer Science. 7314. p. 271. ISBN 978-3-642-30781-2. doi:10.1007/978-3-642-30782-9_19.
↑ Bolch, Gunter; Greiner, Stefan; de Meer, Hermann; Trivedi, Kishor S. (1998). "Steady-State Solutions of Markov Chains". Queueing Networks and Markov Chains. pp. 103–151. ISBN 0471193666. doi:10.1002/0471200581.ch3.
↑ Cox, D. R. (2008). "A use of complex probabilities in the theory of stochastic processes". Mathematical Proceedings of the Cambridge Philosophical Society. 51 (2): 313. doi:10.1017/S0305004100030231.
↑ Osogami, T.; Harchol-Balter, M. (2006). "Closed form solutions for mapping general distributions to quasi-minimal PH distributions". Performance Evaluation. 63 (6): 524. doi:10.1016/j.peva.2005.06.002.
↑ Lang, Andreas; Arthur, Jeffrey L. (1996). "Parameter approximation for Phase-Type distributions". In Chakravarthy, S.; Alfa, Attahiru S. Matrix Analytic methods in Stochastic Models. CRC Press. ISBN 0824797663.
↑ Ramaswami, V.; Poole, D.; Ahn, S.; Byers, S.; Kaplan, A. (2005). "Ensuring Access to Emergency Services in the Presence of Long Internet Dial-Up Calls". Interfaces. 35 (5): 411. doi:10.1287/inte.1050.0155.
↑ Horváth, András S.; Telek, Miklós S. (2002). "PhFit: A General Phase-Type Fitting Tool". Computer Performance Evaluation: Modelling Techniques and Tools. Lecture Notes in Computer Science. 2324. p. 82. ISBN 978-3-540-43539-6. doi:10.1007/3-540-46029-2_5.
↑ Asmussen, Søren; Nerman, Olle; Olsson, Marita (1996). "Fitting Phase-Type Distributions via the EM Algorithm". Scandinavian Journal of Statistics. 23 (4): 419–441. JSTOR 4616418.
↑ Reinecke, P.; Krauß, T.; Wolter, K. (2012). "Cluster-based fitting of phase-type distributions to empirical data". Computers & Mathematics with Applications. 64 (12): 3840. doi:10.1016/j.camwa.2012.03.016.
↑ Pérez, J. F.; Riaño, G. N. (2006). "jPhase: an object-oriented tool for modeling phase-type distributions". Proceeding from the 2006 workshop on Tools for solving structured Markov chains (SMCtools '06) (PDF). ISBN 1595935061. doi:10.1145/1190366.1190370.

M. F. Neuts. Matrix-Geometric Solutions in Stochastic Models: an Algorithmic Approach, Chapter 2: Probability Distributions of Phase Type; Dover Publications Inc., 1981.
G. Latouche, V. Ramaswami. Introduction to Matrix Analytic Methods in Stochastic Modelling, 1st edition. Chapter 2: PH Distributions; ASA SIAM, 1999.
C. A. O'Cinneide (1990). Characterization of phase-type distributions. Communications in Statistics: Stochastic Models, 6(1), 1-57.
C. A. O'Cinneide (1999). Phase-type distribution: open problems and a few properties, Communication in Statistic: Stochastic Models, 15(4), 731-757.

Probability distributions
List
Discrete univariate with finite support	Benford Bernoulli beta-binomial binomial categorical hypergeometric Poisson binomial Rademacher discrete uniform Zipf Zipf–Mandelbrot
Discrete univariate with infinite support	beta negative binomial Borel Conway–Maxwell–Poisson discrete phase-type Delaporte extended negative binomial Gauss–Kuzmin geometric logarithmic negative binomial parabolic fractal Poisson Skellam Yule–Simon zeta
Continuous univariate supported on a bounded interval	arcsine ARGUS Balding–Nichols Bates beta beta rectangular Irwin–Hall Kumaraswamy logit-normal noncentral beta raised cosine reciprocal triangular U-quadratic uniform Wigner semicircle
Continuous univariate supported on a semi-infinite interval	Benini Benktander 1st kind Benktander 2nd kind beta prime Burr chi-squared chi Dagum Davis exponential-logarithmic Erlang exponential F folded normal Flory–Schulz Fréchet gamma gamma/Gompertz generalized inverse Gaussian Gompertz half-logistic half-normal Hotelling's T-squared hyper-Erlang hyperexponential hypoexponential inverse chi-squared scaled inverse chi-squared inverse Gaussian inverse gamma Kolmogorov Lévy log-Cauchy log-Laplace log-logistic log-normal Lomax matrix-exponential Maxwell–Boltzmann Maxwell–Jüttner Mittag-Leffler Nakagami noncentral chi-squared Pareto phase-type poly-Weibull Rayleigh relativistic Breit–Wigner Rice shifted Gompertz truncated normal type-2 Gumbel Weibull Discrete Weibull Wilks's lambda
Continuous univariate supported on the whole real line	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 t type-1 Gumbel Tracy–Widom variance-gamma Voigt
Continuous univariate with support whose type varies	generalized extreme value generalized Pareto Marchenko–Pastur q-exponential q-Gaussian q-Weibull shifted log-logistic Tukey lambda
Mixed continuous-discrete univariate	rectified Gaussian
Multivariate (joint)	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
Directional	Univariate (circular) directional Circular uniform univariate von Mises wrapped normal wrapped Cauchy wrapped exponential wrapped asymmetric Laplace wrapped Lévy Bivariate (spherical) Kent Bivariate (toroidal) bivariate von Mises Multivariate von Mises–Fisher Bingham
Degenerate and singular	Degenerate Dirac delta function Singular Cantor
Families	Circular compound Poisson elliptical exponential natural exponential location–scale maximum entropy mixture Pearson Tweedie wrapped

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