This paper surveys the analysis of parametric Markov models whose transitions are labelled with functions over a finite set of parameters. These models are symbolic representations of uncountable many concrete probabilistic models, each obtained by instantiating the parameters. We consider various analysis problems for a given logical specification φ : do all parameter instantiations within a given region of parameter values satisfy φ ?, which instantiations satisfy φ and which ones do not?, and how can all such instantiations be characterised, either exactly or approximately? We address theoretical complexity results and describe the main ideas underlying state-of-the-art algorithms that established an impressive leap over the last decade enabling the fully automated analysis of models with millions of states and thousands of parameters.
CITATION STYLE
Jansen, N., Junges, S., & Katoen, J. P. (2022). Parameter Synthesis in Markov Models: A Gentle Survey. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13660 LNCS, pp. 407–437). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-22337-2_20
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