Analysis and Prediction of the Long-Run Behavior of Probabilistic Sequential Programs with Recursion (Extended Abstract)

Authors:
Tomas Brazdil;Javier Esparza;Antonin Kucera
Affiliations:
Masaryk University;University of Stuttgart,;Masaryk University
Venue:
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Year:
2005

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Abstract

We introduce a family of long-run average properties of Markov chains that are useful for purposes of performance and reliability analysis, and show that these properties can effectively be checked for a subclass of infinite-state Markov chains generated by probabilistic programs with recursive procedures. We also show how to predict these properties by analyzing finite prefixes of runs, and present an ef?cient prediction algorithm for the mentioned subclass of Markov chains.