Approximation of Discrete Phase-Type Distributions

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Approximation of Discrete Phase-Type Distributions
The analysis of discrete stochastic models such as generally distributed stochastic Petri nets can be done using state space-based methods. The behavior of the model is described by a Markov chain that can be solved mathematically. The phase-type distributions that are used to describe non-Markovian distributions have to be approximated. An approach for the fast and accurate approximation of discrete phase-type distributions is presented. This can be a step towards a practical state space-based simulation method, whereas formerly this approach often had to be discarded as unfeasible due to high memory and runtime costs. Discrete phases also fit in well with current research on proxel-based simulation. They can represent infinite support distribution functions with considerably fewer Markov chain states than proxels. Our hope is that such a combination of both approaches will lead to a competitive simulation algorithm.
Claudia Isensee, Graham Horton
Added 24 Jun 2010
Updated 24 Jun 2010
Type Conference
Year 2005
Where ANSS
Authors Claudia Isensee, Graham Horton
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