Bayesian Markov Blanket Estimation

Dinu Kaufmann, Sonali Parbhoo, Aleksander Wieczorek, Sebastian Keller, David Adametz, Volker Roth
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, PMLR 51:333-341, 2016.

Abstract

This paper considers a Bayesian view for estimating the Markov blanket of a set of query variables, where the set of potential neighbours here is big. We factorize the posterior such that the Markov blanket is conditionally independent of the network of the potential neighbours. By exploiting this blockwise decoupling, we derive analytic expressions for posterior conditionals. Subsequently, we develop an inference scheme, which makes use of the factorization. As a result, estimation of a sub-network is possible without inferring an entire network. Since the resulting Gibbs sampler scales linearly with the number of variables, it can handle relatively large neighbourhoods. The proposed scheme results in faster convergence and superior mixing of the Markov chain than existing Bayesian network estimation techniques.

Cite this Paper

BibTeX
@InProceedings{pmlr-v51-kaufmann16, title = {Bayesian Markov Blanket Estimation}, author = {Kaufmann, Dinu and Parbhoo, Sonali and Wieczorek, Aleksander and Keller, Sebastian and Adametz, David and Roth, Volker}, booktitle = {Proceedings of the 19th International Conference on Artificial Intelligence and Statistics}, pages = {333--341}, year = {2016}, editor = {Gretton, Arthur and Robert, Christian C.}, volume = {51}, series = {Proceedings of Machine Learning Research}, address = {Cadiz, Spain}, month = {09--11 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v51/kaufmann16.pdf}, url = {https://proceedings.mlr.press/v51/kaufmann16.html}, abstract = {This paper considers a Bayesian view for estimating the Markov blanket of a set of query variables, where the set of potential neighbours here is big. We factorize the posterior such that the Markov blanket is conditionally independent of the network of the potential neighbours. By exploiting this blockwise decoupling, we derive analytic expressions for posterior conditionals. Subsequently, we develop an inference scheme, which makes use of the factorization. As a result, estimation of a sub-network is possible without inferring an entire network. Since the resulting Gibbs sampler scales linearly with the number of variables, it can handle relatively large neighbourhoods. The proposed scheme results in faster convergence and superior mixing of the Markov chain than existing Bayesian network estimation techniques.} }
Endnote
%0 Conference Paper %T Bayesian Markov Blanket Estimation %A Dinu Kaufmann %A Sonali Parbhoo %A Aleksander Wieczorek %A Sebastian Keller %A David Adametz %A Volker Roth %B Proceedings of the 19th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2016 %E Arthur Gretton %E Christian C. Robert %F pmlr-v51-kaufmann16 %I PMLR %P 333--341 %U https://proceedings.mlr.press/v51/kaufmann16.html %V 51 %X This paper considers a Bayesian view for estimating the Markov blanket of a set of query variables, where the set of potential neighbours here is big. We factorize the posterior such that the Markov blanket is conditionally independent of the network of the potential neighbours. By exploiting this blockwise decoupling, we derive analytic expressions for posterior conditionals. Subsequently, we develop an inference scheme, which makes use of the factorization. As a result, estimation of a sub-network is possible without inferring an entire network. Since the resulting Gibbs sampler scales linearly with the number of variables, it can handle relatively large neighbourhoods. The proposed scheme results in faster convergence and superior mixing of the Markov chain than existing Bayesian network estimation techniques.
RIS
TY - CPAPER TI - Bayesian Markov Blanket Estimation AU - Dinu Kaufmann AU - Sonali Parbhoo AU - Aleksander Wieczorek AU - Sebastian Keller AU - David Adametz AU - Volker Roth BT - Proceedings of the 19th International Conference on Artificial Intelligence and Statistics DA - 2016/05/02 ED - Arthur Gretton ED - Christian C. Robert ID - pmlr-v51-kaufmann16 PB - PMLR DP - Proceedings of Machine Learning Research VL - 51 SP - 333 EP - 341 L1 - http://proceedings.mlr.press/v51/kaufmann16.pdf UR - https://proceedings.mlr.press/v51/kaufmann16.html AB - This paper considers a Bayesian view for estimating the Markov blanket of a set of query variables, where the set of potential neighbours here is big. We factorize the posterior such that the Markov blanket is conditionally independent of the network of the potential neighbours. By exploiting this blockwise decoupling, we derive analytic expressions for posterior conditionals. Subsequently, we develop an inference scheme, which makes use of the factorization. As a result, estimation of a sub-network is possible without inferring an entire network. Since the resulting Gibbs sampler scales linearly with the number of variables, it can handle relatively large neighbourhoods. The proposed scheme results in faster convergence and superior mixing of the Markov chain than existing Bayesian network estimation techniques. ER -
APA
Kaufmann, D., Parbhoo, S., Wieczorek, A., Keller, S., Adametz, D. & Roth, V.. (2016). Bayesian Markov Blanket Estimation. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 51:333-341 Available from https://proceedings.mlr.press/v51/kaufmann16.html.

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