Numerical Recipes in C: The Art of Scientific Computing
Numerical Recipes in C: The Art of Scientific Computing
PhFit: A General Phase-Type Fitting Tool
TOOLS '02 Proceedings of the 12th International Conference on Computer Performance Evaluation, Modelling Techniques and Tools
The Scale Factor: A New Degree of Freedom in Phase Type Approximation
DSN '02 Proceedings of the 2002 International Conference on Dependable Systems and Networks
Acyclic discrete phase type distributions: properties and a parameter estimation algorithm
Performance Evaluation
A New Approach for Computing Conditional Probabilities of General Stochastic Processes
ANSS '06 Proceedings of the 39th annual Symposium on Simulation
IEEE Transactions on Information Theory
Proceedings of the Winter Simulation Conference
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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 approacheswill lead to a competitive simulation algorithm.