Symbolic representations and analysis of large probabilistic systems

A Miner; David Parker; C Baier; B Haverkort; H Hermanns; J-P Katoen; M Siegle; F Vaandrager

Symbolic representations and analysis of large probabilistic systems

A Miner, David Parker, C Baier, B Haverkort, H Hermanns, J-P Katoen, M Siegle, F Vaandrager

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter

41 Citations (Scopus)

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.

Original language	English
Title of host publication	Validation of Stochastic Systems: A Guide to Current Research
Publication status	Published - 1 Jan 2004

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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.",

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AU - Miner, A

AU - Parker, David

AU - Baier, C

AU - Haverkort, B

AU - Hermanns, H

AU - Katoen, J-P

AU - Siegle, M

AU - Vaandrager, F

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N2 - 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.

AB - 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.

M3 - Chapter

BT - Validation of Stochastic Systems: A Guide to Current Research

ER -

Symbolic representations and analysis of large probabilistic systems

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