Uncertainty in Artificial Intelligence
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Unsupervised Learning of Noisy-Or Bayesian Networks
Yonatan Halpern, David Sontag
Abstract:
This paper considers the problem of learning the parameters in Bayesian networks of discrete variables with known structure and hidden variables. Previous approaches in these settings typically use expectation maximization; when the network has high treewidth, the required expectations might be approximated using Monte Carlo or variational methods. We show how to avoid inference altogether during learning by giving a polynomial-time algorithm based on the method-of-moments, building upon recent work on learning discrete-valued mixture models. In particular, we show how to learn the parameters for a family of bipartite noisy-or Bayesian networks. In our experimental results, we demonstrate an application of our algorithm to learning QMR-DT, a large Bayesian network used for medical diagnosis. We show that it is possible to fully learn the parameters of QMR-DT even when only the findings are observed in the training data (ground truth diseases unknown).
Keywords:
Pages: 272-281
PS Link:
PDF Link: /papers/13/p272-halpern.pdf
BibTex:
@INPROCEEDINGS{Halpern13,
AUTHOR = "Yonatan Halpern and David Sontag",
TITLE = "Unsupervised Learning of Noisy-Or Bayesian Networks",
BOOKTITLE = "Proceedings of the Twenty-Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-13)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2013",
PAGES = "272--281"
}


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