Computing Quantiles in Markov Reward Models

Michael UmmelsMichael Ummels, Christel BaierChristel Baier

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Michael Ummels, Christel Baier
Computing Quantiles in Markov Reward Models
Proc. of the 16th International Conference on Foundations of Software Science and Computation Structures (FOSSACS), volume 7794 of Lecture Notes in Computer Science, 353--368, 2013. Springer

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KurzfassungAbstract
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.
Forschungsgruppe:Research Group: Algebraische und logische Grundlagen der InformatikAlgebraic and Logical Foundations of Computer Science

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-37075-5_23.

@inproceedings{UB2013,
  author    = {Michael Ummels and Christel Baier},
  title     = {Computing Quantiles in Markov Reward Models},
  booktitle = {Proc. of the 16th International Conference on Foundations of
               Software Science and Computation Structures (FOSSACS)},
  series    = {Lecture Notes in Computer Science},
  volume    = {7794},
  publisher = {Springer},
  year      = {2013},
  pages     = {353--368},
  doi       = {10.1007/978-3-642-37075-5_23}
}