Partial Order MCMC for Structure Discovery in Bayesian Networks

Teppo Niinimaki, Pekka Parviainen, Mikko Koivisto
Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:623-630, 2011.

Abstract

We present a new Markov chain Monte Carlo method for estimating posterior probabilities of structural features in Bayesian networks. The method draws samples from the posterior distribution of partial orders on the nodes; for each sampled partial order, the conditional probabilities of interest are computed exactly. We give both analytical and empirical results that suggest the superiority of the new method compared to previous methods, which sample either directed acyclic graphs or linear orders on the nodes.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR9-niinimaki11a, title = {Partial Order {MCMC} for Structure Discovery in {B}ayesian Networks}, author = {Niinimaki, Teppo and Parviainen, Pekka and Koivisto, Mikko}, booktitle = {Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence}, pages = {623--630}, year = {2011}, editor = {Cozman, Fabio and Pfeffer, Avi}, volume = {R9}, series = {Proceedings of Machine Learning Research}, month = {14--17 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r9/main/assets/niinimaki11a/niinimaki11a.pdf}, url = {https://proceedings.mlr.press/r9/niinimaki11a.html}, abstract = {We present a new Markov chain Monte Carlo method for estimating posterior probabilities of structural features in Bayesian networks. The method draws samples from the posterior distribution of partial orders on the nodes; for each sampled partial order, the conditional probabilities of interest are computed exactly. We give both analytical and empirical results that suggest the superiority of the new method compared to previous methods, which sample either directed acyclic graphs or linear orders on the nodes.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Partial Order MCMC for Structure Discovery in Bayesian Networks %A Teppo Niinimaki %A Pekka Parviainen %A Mikko Koivisto %B Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2011 %E Fabio Cozman %E Avi Pfeffer %F pmlr-vR9-niinimaki11a %I PMLR %P 623--630 %U https://proceedings.mlr.press/r9/niinimaki11a.html %V R9 %X We present a new Markov chain Monte Carlo method for estimating posterior probabilities of structural features in Bayesian networks. The method draws samples from the posterior distribution of partial orders on the nodes; for each sampled partial order, the conditional probabilities of interest are computed exactly. We give both analytical and empirical results that suggest the superiority of the new method compared to previous methods, which sample either directed acyclic graphs or linear orders on the nodes. %Z Reissued by PMLR on 04 October 2026.
APA
Niinimaki, T., Parviainen, P. & Koivisto, M.. (2011). Partial Order MCMC for Structure Discovery in Bayesian Networks. Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R9:623-630 Available from https://proceedings.mlr.press/r9/niinimaki11a.html. Reissued by PMLR on 04 October 2026.

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