Abstract
In this paper we consider existing and new representation transformation methods for non-Markovian generalizations of Markov chain driven stochastic models which intend transforming non-Markovian representations into Markovian ones and evaluate their efficiency through numerical experiments. One of the new features of the considered methods is the ability to obtain a Markovian representation of larger size. © 2013 Springer-Verlag.
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CITATION STYLE
Mészáros, A., Horváth, G., & Telek, M. (2013). Representation transformations for finding Markovian representations. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7984 LNCS, pp. 277–291). https://doi.org/10.1007/978-3-642-39408-9_20
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