Case-based Probability Factoring in Bayesian Belief Networks

Luigi Portinale
Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, PMLR R1:391-398, 1997.

Abstract

Bayesian network inference can be formulated as a combinatorial optimization problem, concerning in the computation of an optimal factoring for the distribution represented in the net. Since the determination of an optimal factoring is a computationally hard problem, heuristic greedy strategies able to find approximations of the optimal factoring are usually adopted. In the present paper we investigate an alternative approach based on a combination of genetic algorithms (GA) and case-based reasoning (CBR). We show how the use of genetic algorithms can improve the quality of the computed factoring in case a static strategy is used (as for the MPE computation), while the combination of GA and CBR can still provide advantages in the case of dynamic strategies. Some preliminary results on different kinds of nets are then reported.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR1-portinale97a, title = {Case-based Probability Factoring in Bayesian Belief Networks}, author = {Portinale, Luigi}, booktitle = {Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics}, pages = {391--398}, year = {1997}, editor = {Madigan, David and Smyth, Padhraic}, volume = {R1}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/r1/portinale97a/portinale97a.pdf}, url = {https://proceedings.mlr.press/r1/portinale97a.html}, abstract = {Bayesian network inference can be formulated as a combinatorial optimization problem, concerning in the computation of an optimal factoring for the distribution represented in the net. Since the determination of an optimal factoring is a computationally hard problem, heuristic greedy strategies able to find approximations of the optimal factoring are usually adopted. In the present paper we investigate an alternative approach based on a combination of genetic algorithms (GA) and case-based reasoning (CBR). We show how the use of genetic algorithms can improve the quality of the computed factoring in case a static strategy is used (as for the MPE computation), while the combination of GA and CBR can still provide advantages in the case of dynamic strategies. Some preliminary results on different kinds of nets are then reported.}, note = {Reissued by PMLR on 30 March 2021.} }
Endnote
%0 Conference Paper %T Case-based Probability Factoring in Bayesian Belief Networks %A Luigi Portinale %B Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1997 %E David Madigan %E Padhraic Smyth %F pmlr-vR1-portinale97a %I PMLR %P 391--398 %U https://proceedings.mlr.press/r1/portinale97a.html %V R1 %X Bayesian network inference can be formulated as a combinatorial optimization problem, concerning in the computation of an optimal factoring for the distribution represented in the net. Since the determination of an optimal factoring is a computationally hard problem, heuristic greedy strategies able to find approximations of the optimal factoring are usually adopted. In the present paper we investigate an alternative approach based on a combination of genetic algorithms (GA) and case-based reasoning (CBR). We show how the use of genetic algorithms can improve the quality of the computed factoring in case a static strategy is used (as for the MPE computation), while the combination of GA and CBR can still provide advantages in the case of dynamic strategies. Some preliminary results on different kinds of nets are then reported. %Z Reissued by PMLR on 30 March 2021.
APA
Portinale, L.. (1997). Case-based Probability Factoring in Bayesian Belief Networks. Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R1:391-398 Available from https://proceedings.mlr.press/r1/portinale97a.html. Reissued by PMLR on 30 March 2021.

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