A Study of Approximate Inference in Probabilistic Relational Models

Fabian Kaelin, Doina Precup
Proceedings of 2nd Asian Conference on Machine Learning, PMLR 13:315-330, 2010.

Abstract

We tackle the problem of approximate inference in Probabilistic Relational Models (PRMs) and propose the Lazy Aggregation Block Gibbs (LABG) algorithm. The LABG algorithm makes use of the inherent relational structure of the ground Bayesian network corresponding to a PRM. We evaluate our approach on artificial and real data and show that it scales well with the size of the data set.

Cite this Paper

BibTeX
@InProceedings{pmlr-v13-kaelin10a, title = {A Study of Approximate Inference in Probabilistic Relational Models}, author = {Kaelin, Fabian and Precup, Doina}, booktitle = {Proceedings of 2nd Asian Conference on Machine Learning}, pages = {315--330}, year = {2010}, editor = {Sugiyama, Masashi and Yang, Qiang}, volume = {13}, series = {Proceedings of Machine Learning Research}, address = {Tokyo, Japan}, month = {08--10 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v13/kaelin10a/kaelin10a.pdf}, url = {https://proceedings.mlr.press/v13/kaelin10a.html}, abstract = {We tackle the problem of approximate inference in Probabilistic Relational Models (PRMs) and propose the Lazy Aggregation Block Gibbs (LABG) algorithm. The LABG algorithm makes use of the inherent relational structure of the ground Bayesian network corresponding to a PRM. We evaluate our approach on artificial and real data and show that it scales well with the size of the data set.} }
Endnote
%0 Conference Paper %T A Study of Approximate Inference in Probabilistic Relational Models %A Fabian Kaelin %A Doina Precup %B Proceedings of 2nd Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2010 %E Masashi Sugiyama %E Qiang Yang %F pmlr-v13-kaelin10a %I PMLR %P 315--330 %U https://proceedings.mlr.press/v13/kaelin10a.html %V 13 %X We tackle the problem of approximate inference in Probabilistic Relational Models (PRMs) and propose the Lazy Aggregation Block Gibbs (LABG) algorithm. The LABG algorithm makes use of the inherent relational structure of the ground Bayesian network corresponding to a PRM. We evaluate our approach on artificial and real data and show that it scales well with the size of the data set.
RIS
TY - CPAPER TI - A Study of Approximate Inference in Probabilistic Relational Models AU - Fabian Kaelin AU - Doina Precup BT - Proceedings of 2nd Asian Conference on Machine Learning DA - 2010/10/31 ED - Masashi Sugiyama ED - Qiang Yang ID - pmlr-v13-kaelin10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 13 SP - 315 EP - 330 L1 - http://proceedings.mlr.press/v13/kaelin10a/kaelin10a.pdf UR - https://proceedings.mlr.press/v13/kaelin10a.html AB - We tackle the problem of approximate inference in Probabilistic Relational Models (PRMs) and propose the Lazy Aggregation Block Gibbs (LABG) algorithm. The LABG algorithm makes use of the inherent relational structure of the ground Bayesian network corresponding to a PRM. We evaluate our approach on artificial and real data and show that it scales well with the size of the data set. ER -
APA
Kaelin, F. & Precup, D.. (2010). A Study of Approximate Inference in Probabilistic Relational Models. Proceedings of 2nd Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 13:315-330 Available from https://proceedings.mlr.press/v13/kaelin10a.html.

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