Structured representation of complex stochastic systems

Nir Friedman*, Daphne Koller, Avi Pfeffer

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

10 Scopus citations

Abstract

This paper considers the problem of representing complex systems that evolve stochastically over time. Dynamic Bayesian networks provide a compact representation for stochastic processes. Unfortunately, they are often unwieldy since they cannot explicitly model the complex organizational structure of many real life systems: the fact that processes are typically composed of several interacting subprocesses, each of which can, in turn, be further decomposed. We propose a hierarchically structured representation language which extends both dynamic Bayesian networks and the object-oriented Bayesian network framework of [9], and show that our language allows us to describe such systems in a natural and modular way. Our language supports a natural representation for certain system characteristics that are hard to capture using more traditional frameworks. For example, it allows us to represent systems where some processes evolve at a different rate than others, or systems where the processes interact only intermittently. We provide a simple inference mechanism for our representation via translation to Bayesian networks, and suggest ways in which the inference algorithm can exploit the additional structure encoded in our representation.

Original languageAmerican English
Pages157-164
Number of pages8
StatePublished - 1998
Externally publishedYes
EventProceedings of the 1998 15th National Conference on Artificial Intelligence, AAAI - Madison, WI, USA
Duration: 26 Jul 199830 Jul 1998

Conference

ConferenceProceedings of the 1998 15th National Conference on Artificial Intelligence, AAAI
CityMadison, WI, USA
Period26/07/9830/07/98

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