Uncertainty in Artificial Intelligence
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A Differential Approach to Inference in Bayesian Networks
Adnan Darwiche
Abstract:
We present a new approach for inference in Bayesian networks, which is mainly based on partial differentiation. According to this approach, one compiles a Bayesian network into a multivariate polynomial and then computes the partial derivatives of this polynomial with respect to each variable. We show that once such derivatives are made available, one can compute in constant-time answers to a large class of probabilistic queries, which are central to classical inference, parameter estimation, model validation and sensitivity analysis. We present a number of complexity results relating to the compilation of such polynomials and to the computation of their partial derivatives. We argue that the combined simplicity, comprehensiveness and computational complexity of the presented framework is unique among existing frameworks for inference in Bayesian networks.
Keywords:
Pages: 123-132
PS Link: http://www.cs.auc.dk/research/DSS/papers/darwiche1.ps
PDF Link: /papers/00/p123-darwiche.pdf
BibTex:
@INPROCEEDINGS{Darwiche00,
AUTHOR = "Adnan Darwiche ",
TITLE = "A Differential Approach to Inference in Bayesian Networks",
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 = "123--132"
}


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