Results 261 to 270 of about 287,761 (311)

Examples of fitting structured phase–type distributions

Applied Stochastic Models and Data Analysis, 1994
AbstractA sub–class of phase–type distributions is defined in terms of a Markov process with sequential transitions between transient states and transitions from these states to absorption. Such distributions form a very rich class; they can be fitted to data, and any structure revealed by the parameter estimates used to develop more parsimonious re ...
M J Faddy
exaly   +4 more sources

Multivariate Phase-Type Distributions

Operations Research, 1984
A (univariate) random variable is said to be of phase type if it can be represented as the time until absorption in a finite state absorbing Markov chain. Univariate phase type random variables are useful because they arise from processes that are often encountered in applications, they have densities that can be written in a closed form, they possess
David Assaf   +3 more
openaire   +1 more source

Variational Bayes for Phase-Type Distribution

Communications in Statistics - Simulation and Computation, 2014
This article develops an algorithm for estimating parameters of general phase-type (PH) distribution based on Bayes estimation. The idea of Bayes estimation is to regard parameters as random variables, and the posterior distribution of parameters which is updated by the likelihood function provides estimators of parameters.
Hiroyuki Okamura   +2 more
openaire   +1 more source

Bilateral phase‐type distributions

Naval Research Logistics Quarterly, 1985
AbstractIn this article we define a class of distributions called bilateral phase type (BPH), and study its closure and computational properties. The class of BPH distributions is closed under convolution, negative convolution, and mixtures. The one‐sided version of BPH, called generalized phase type (GPH), is also defined.
openaire   +1 more source

Graph-based algorithms for phase-type distributions

Statistics and Computing, 2022
Abstract Phase-type distributions model the time until absorption in continuous or discrete-time Markov chains on a finite state space. The multivariate phase-type distributions have diverse and important applications by modeling rewards accumulated at visited states.
Tobias Røikjer   +2 more
openaire   +1 more source

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