Uncertainty in Artificial Intelligence
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Automated Analytic Asymptotic Evaluation of the Marginal Likelihood for Latent Models
Dmitry Rusakov, Dan Geiger
Abstract:
We present and implement two algorithms for analytic asymptotic evaluation of the marginal likelihood of data given a Bayesian network with hidden nodes. As shown by previous work, this evaluation is particularly hard for latent Bayesian network models, namely networks that include hidden variables, where asymptotic approximation deviates from the standard BIC score. Our algorithms solve two central difficulties in asymptotic evaluation of marginal likelihood integrals, namely, evaluation of regular dimensionality drop for latent Bayesian network models and computation of non-standard approximation formulas for singular statistics for these models. The presented algorithms are implemented in Matlab and Maple and their usage is demonstrated for marginal likelihood approximations for Bayesian networks with hidden variables
Keywords:
Pages: 501-508
PS Link:
PDF Link: /papers/03/p501-rusakov.pdf
BibTex:
@INPROCEEDINGS{Rusakov03,
AUTHOR = "Dmitry Rusakov and Dan Geiger",
TITLE = "Automated Analytic Asymptotic Evaluation of the Marginal Likelihood for Latent Models",
BOOKTITLE = "Proceedings of the Nineteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-03)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2003",
PAGES = "501--508"
}


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