Results 251 to 260 of about 11,382,550 (291)

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, Ryo Watanabe, Tadashi Dohi   +2 more
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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.
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Phase-Type Distributions

2014
Continuous-time Markov chainsContinuous-time Markov chain (CTMCs)CTMC seealso Continuous-time Markov chain Markov chain seealso Continuous-time Markov chain are a class of stochastic processes with a discrete state space in which the time between transitions follows an exponential distribution.
Peter Buchholz, Jan Kriege, Iryna Felko
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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, Asger Hobolth, Kasper Munch   +2 more
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Multivariate finite-support phase-type distributions

Journal of Applied Probability, 2020
AbstractWe introduce a multivariate class of distributions with support I, a k-orthotope in $[0,\infty)^{k}$ , which is dense in the set of all k-dimensional distributions with support I. We call this new class ‘multivariate finite-support phase-type distributions’ (MFSPH). Though we generally define MFSPH distributions on any finite k-orthotope in $[
Celeste R. Pavithra, T. G. Deepak
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Bilateral Phase Type Distributions

Stochastic Models, 2005
Abstract A new class of probability distributions called “bilateral phase type distributions (BPH)” on (−∞, ∞) is defined as a generalization of the versatile class of phase type (PH) distributions on [0, ∞) introduced by Marcel F. Neuts. We derive the basic descriptors of such distributions in an algorithmically tractable manner and show that this ...
Soohan Ahn, V. Ramaswami
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Estimation of Phase-Type Distributions

2017
This chapter deals with the estimation of phase-type distributions in a number of different circumstances. First, we consider their estimation when only absorption times are available, and provide both EM and MCMC approaches, which in turn complement each other, aiming at different purposes. Then we consider censored data of a different kind, which can
Mogens Bladt, Bo Friis Nielsen
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Moment distributions of phase-type (abstract only)

ACM SIGMETRICS Performance Evaluation Review, 2012
Both matrix-exponential and phase-type distributions have a number of important closure properties. Among those are the distributions of the age and residual life-time of a stationary renewal process with inter-arrivals of either type. In this talk we show that the spread, which is the sum of the age an residual life-time, is also phase-type ...
Mogens Bladt, Bo Friis Nielsen
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Characterization of phase-type distributions

Communications in Statistics. Stochastic Models, 1990
A distribution with rational Laplace-Stieltjes transform is of phase type if and only if it is either the point mass at zero, or it has a continuous positive density on the positive reals and its Laplace-Stieltjes transform has a unique pole of maximal real part (which is therefore real). This result is proved, and the corresponding characterization of
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Lévy Processes, Phase-Type Distributions, and Martingales

Stochastic Models, 2014
Levy processes are defined as processes with stationary independent increments and have become increasingly popular as models in queueing, finance, etc.; apart from Brownian motion and compound Poisson processes, some popular examples are stable processes, variance gamma processes, CGMY Levy processes (tempered stable processes), NIG (normal inverse ...
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