Uncertainty in Artificial Intelligence
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Expectation Maximization and Complex Duration Distributions for Continuous Time Bayesian Networks
Uri Nodelman, Christian Shelton, Daphne Koller
Abstract:
Continuous time Bayesian networks (CTBNs) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cyclic) dependency graph over a set of variables, each of which represents a finite state continuous time Markov process whose transition model is a function of its parents. We address the problem of learning the parameters and structure of a CTBN from partially observed data. We show how to apply expectation maximization (EM) and structural expectation maximization (SEM) to CTBNs. The availability of the EM algorithm allows us to extend the representation of CTBNs to allow a much richer class of transition durations distributions, known as phase distributions. This class is a highly expressive semi-parametric representation, which can approximate any duration distribution arbitrarily closely. This extension to the CTBN framework addresses one of the main limitations of both CTBNs and DBNs - the restriction to exponentially / geometrically distributed duration. We present experimental results on a real data set of people's life spans, showing that our algorithm learns reasonable models - structure and parameters - from partially observed data, and, with the use of phase distributions, achieves better performance than DBNs.
Keywords:
Pages: 421-430
PS Link:
PDF Link: /papers/05/p421-nodelman.pdf
BibTex:
@INPROCEEDINGS{Nodelman05,
AUTHOR = "Uri Nodelman and Christian Shelton and Daphne Koller",
TITLE = "Expectation Maximization and Complex Duration Distributions for Continuous Time Bayesian Networks",
BOOKTITLE = "Proceedings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-05)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2005",
PAGES = "421--430"
}


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