[MP04] Andrew Miner and David Parker. Symbolic Representations and Analysis of Large Probabilistic Systems. In C. Baier, B. Haverkort, H. Hermanns, J-P. Katoen, M. Siegle and F. Vaandrager (editors), Validation of Stochastic Systems: A Guide to Current Research, volume 2925 of Lecture Notes in Computer Science, pages 296-338. August 2004. [ps.gz] [pdf] [bib] [Tutorial/survey paper on symbolic methods for probabilistic verification, including the (MD)BDD-based methods in PRISM.]
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Front cover Abstract. This paper describes symbolic techniques for the construction, representation and analysis of large, probabilistic systems. Symbolic approaches derive their efficiency by exploiting high-level structure and regularity in the models to which they are applied, increasing the size of the state spaces which can be tackled. In general, this is done by using data structures which provide compact storage but which are still efficient to manipulate, usually based on binary decision diagrams (BDDs) or their extensions. In this paper we focus on BDDs, multi-valued decision diagrams (MDDs), multi-terminal binary decision diagrams (MTBDDs) and matrix diagrams.