A Study of Approximate Inference in Probabilistic Relational Models

Fabian Kaelin, Doina Precup
; Proceedings of 2nd Asian Conference on Machine Learning, JMLR Workshop and Conference Proceedings 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 = {Fabian Kaelin and Doina Precup}, booktitle = {Proceedings of 2nd Asian Conference on Machine Learning}, pages = {315--330}, year = {2010}, editor = {Masashi Sugiyama and Qiang Yang}, volume = {13}, series = {Proceedings of Machine Learning Research}, address = {Tokyo, Japan}, month = {08--10 Nov}, publisher = {JMLR Workshop and Conference Proceedings}, pdf = {http://proceedings.mlr.press/v13/kaelin10a/kaelin10a.pdf}, url = {http://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 %J Proceedings of Machine Learning Research %P 315--330 %U http://proceedings.mlr.press %V 13 %W PMLR %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 PY - 2010/10/31 DA - 2010/10/31 ED - Masashi Sugiyama ED - Qiang Yang ID - pmlr-v13-kaelin10a PB - PMLR SP - 315 DP - PMLR EP - 330 L1 - http://proceedings.mlr.press/v13/kaelin10a/kaelin10a.pdf UR - http://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 PMLR 13:315-330

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