Uncertainty in Artificial Intelligence
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Dynamic Bayesian Multinets
Jeff Bilmes
Abstract:
In this work, dynamic Bayesian multinets are introduced where a Markov chain state at time t determines conditional independence patterns between random variables lying within a local time window surrounding t. It is shown how information-theoretic criterion functions can be used to induce sparse, discriminative, and class-conditional network structures that yield an optimal approximation to the class posterior probability, and therefore are useful for the classification task. Using a new structure learning heuristic, the resulting models are tested on a medium-vocabulary isolated-word speech recognition task. It is demonstrated that these discriminatively structured dynamic Bayesian multinets, when trained in a maximum likelihood setting using EM, can outperform both HMMs and other dynamic Bayesian networks with a similar number of parameters.
Keywords: multinets, discriminability, structure learning, classification, HMM, BMM, DBN, Bayes
Pages: 38-45
PS Link:
PDF Link: /papers/00/p38-bilmes.pdf
BibTex:
@INPROCEEDINGS{Bilmes00,
AUTHOR = "Jeff Bilmes ",
TITLE = "Dynamic Bayesian Multinets",
BOOKTITLE = "Proceedings of the Sixteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-00)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2000",
PAGES = "38--45"
}


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