Validation of Stochastic Systems

Volume 2925 of the series Lecture Notes in Computer Science pp 296-338

Symbolic Representations and Analysis of Large Probabilistic Systems

  • Andrew MinerAffiliated withDept. of Computer Science, Iowa State University
  • , David ParkerAffiliated withSchool of Computer Science, University of Birmingham

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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.