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 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 non-negative 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 time polynomial in the size of the chain and the maximum reward.Comment: 17 pages, 1 figure; typo in example correcte

Formal Techniques for Computer Systems and Business Processes, European Performance Engineering Workshop, EPEW 2005 and International Workshop on Web Services and Formal Methods, WS-FM 2005

The proceedings contain 23 papers. The topics discussed include: implicit representations and algorithms for the logic and stochastic analysis of discrete-state systems; on moments of discrete phase-type distributions; zero-automatic queues; a unified approach to the moments based distribution estimation - unbounded support; bounds for point and steady-state availability: an algorithmic approach based on lumpability and stochastic ordering; stochastic model checking with stochastic comparison; hypergraph partitioning for faster parallel pagerank computation; prediction of communication latency over complex network behaviors on SMP clusters; and from theory to practice in transactional composition of web services