Unsupervised Learning of Noisy-OR Bayesian Networks

Yonatan Halpern, David Sontag
Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:422-431, 2013.

Abstract

This paper considers the problem of learn- ing 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 vari- ational methods. We show how to avoid inference altogether during learning by giv- ing a polynomial-time algorithm based on the method-of-moments, building upon re- cent work on learning discrete-valued mix- ture models. In particular, we show how to learn the parameters for a family of bipartite noisy-or Bayesian networks. In our experi- mental results, we demonstrate an applica- tion of our algorithm to learning QMR-DT, a large Bayesian network used for medical di- agnosis. 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).

Cite this Paper

BibTeX
@InProceedings{pmlr-vR11-halpern13a, title = {Unsupervised Learning of Noisy-{OR} {B}ayesian Networks}, author = {Halpern, Yonatan and Sontag, David}, booktitle = {Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence}, pages = {422--431}, year = {2013}, editor = {Nicholson, Ann and Smyth, Padhraic}, volume = {R11}, series = {Proceedings of Machine Learning Research}, month = {12--14 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r11/main/assets/halpern13a/halpern13a.pdf}, url = {https://proceedings.mlr.press/r11/halpern13a.html}, abstract = {This paper considers the problem of learn- ing 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 vari- ational methods. We show how to avoid inference altogether during learning by giv- ing a polynomial-time algorithm based on the method-of-moments, building upon re- cent work on learning discrete-valued mix- ture models. In particular, we show how to learn the parameters for a family of bipartite noisy-or Bayesian networks. In our experi- mental results, we demonstrate an applica- tion of our algorithm to learning QMR-DT, a large Bayesian network used for medical di- agnosis. 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).}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Unsupervised Learning of Noisy-OR Bayesian Networks %A Yonatan Halpern %A David Sontag %B Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2013 %E Ann Nicholson %E Padhraic Smyth %F pmlr-vR11-halpern13a %I PMLR %P 422--431 %U https://proceedings.mlr.press/r11/halpern13a.html %V R11 %X This paper considers the problem of learn- ing 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 vari- ational methods. We show how to avoid inference altogether during learning by giv- ing a polynomial-time algorithm based on the method-of-moments, building upon re- cent work on learning discrete-valued mix- ture models. In particular, we show how to learn the parameters for a family of bipartite noisy-or Bayesian networks. In our experi- mental results, we demonstrate an applica- tion of our algorithm to learning QMR-DT, a large Bayesian network used for medical di- agnosis. 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). %Z Reissued by PMLR on 04 October 2026.
APA
Halpern, Y. & Sontag, D.. (2013). Unsupervised Learning of Noisy-OR Bayesian Networks. Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R11:422-431 Available from https://proceedings.mlr.press/r11/halpern13a.html. Reissued by PMLR on 04 October 2026.

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