Structured representation of complex stochastic systems (1998)by N. Friedman, D. Koller, and A. Pfeffer
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 objectoriented Bayesian network framework of [Koller & Pfeffer, 1997], 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.
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N. Friedman, D. Koller, and A. Pfeffer (1998). "Structured representation of complex stochastic systems." Proceedings of the 15th National Conference on Artificial Intelligence (AAAI) (pp. 157164).


Bibtex citation
@inproceedings{Friedman+al:AAAI98,
author = "N. Friedman and D. Koller and A. Pfeffer",
booktitle = "Proceedings of the 15th National Conference on
Artificial Intelligence (AAAI)",
title = "Structured representation of complex stochastic
systems",
pages = "157164",
year = "1998",
}
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