
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–parametrizations. Several example data sets are used as illustrations.
Markov processes, Structure, phase-type probability distributions, 2611 Modelling and Simulation, Distribution theory, Queueing theory (aspects of probability theory), sequential transitions, Phase–type probability distributions, Sequential transitions, Phase-type probability distributions, 1405 Management of Technology and Innovation, Markov process, structure, mixture models, Mixture models
Markov processes, Structure, phase-type probability distributions, 2611 Modelling and Simulation, Distribution theory, Queueing theory (aspects of probability theory), sequential transitions, Phase–type probability distributions, Sequential transitions, Phase-type probability distributions, 1405 Management of Technology and Innovation, Markov process, structure, mixture models, Mixture models
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