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Computing Quantiles in Markov Reward Models

Probabilistic model checking mainly concentrates on techniques for reasoning about the probabilities of certain path properties or expected values of certain random variables. For the quantitative system analysis, however, there is also another type of interesting performance measure, namely quantiles. A typical quantile query takes as input a lower probability bound p ∈ ]0,1] and a reachability property. The task is then to compute the minimal reward bound r such that with probability at least p the target set will be reached before the accumulated reward exceeds r. Quantiles are well-known from mathematical statistics, but to the best of our knowledge they have not been addressed by the model checking community so far.

In this paper, we study the complexity of quantile queries for until properties in discrete-time finite-state Markov decision processes with nonnegative rewards on states. We show that qualitative quantile queries can be evaluated in polynomial time and present an exponential algorithm for the evaluation of quantitative quantile queries. For the special case of Markov chains, we show that quantitative quantile queries can be evaluated in pseudo-polynomial time.

Identiferoai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:28185
Date January 2013
CreatorsUmmels, Michael, Baier, Christel
PublisherTechnische Universität Dresden
Source SetsHochschulschriftenserver (HSSS) der SLUB Dresden
LanguageEnglish
Detected LanguageEnglish
Typedoc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text
SourceFoundations of Software Science and Computation Structures, Seiten 353-368. Springer. FOSSACS 2013, 18.-20. März 2013, Rom, Italien. ISBN 978 3 642 37074 8. ISSN 0302-9743
Rightsinfo:eu-repo/semantics/openAccess
Relation10.1007/978-3-642-37075-5_23

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