Inference for multiplicative models

Ydo Wexler, Christopher Meek
Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:595-602, 2008.

Abstract

The paper introduces a generalization for known probabilistic models such as log-linear and graphical models, called here multiplicative models. These models, that express probabilities via product of parameters are shown to capture multiple forms of contextual independence between variables, including decision graphs and noisy-OR functions. An inference algorithm for multiplicative models is provided and its correctness is proved. The complexity analysis of the inference algorithm uses a more refined parameter than the tree-width of the underlying graph, and shows the computational cost does not exceed that of the variable elimination algorithm in graphical models. The paper ends with examples where using the new models and algorithm is computationally beneficial.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR6-wexler08a, title = {Inference for multiplicative models}, author = {Wexler, Ydo and Meek, Christopher}, booktitle = {Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence}, pages = {595--602}, year = {2008}, editor = {McAllester, David A. and Myllymäki, Petri}, volume = {R6}, series = {Proceedings of Machine Learning Research}, month = {09--12 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r6/main/assets/wexler08a/wexler08a.pdf}, url = {https://proceedings.mlr.press/r6/wexler08a.html}, abstract = {The paper introduces a generalization for known probabilistic models such as log-linear and graphical models, called here multiplicative models. These models, that express probabilities via product of parameters are shown to capture multiple forms of contextual independence between variables, including decision graphs and noisy-OR functions. An inference algorithm for multiplicative models is provided and its correctness is proved. The complexity analysis of the inference algorithm uses a more refined parameter than the tree-width of the underlying graph, and shows the computational cost does not exceed that of the variable elimination algorithm in graphical models. The paper ends with examples where using the new models and algorithm is computationally beneficial.}, note = {Reissued by PMLR on 09 October 2024.} }
Endnote
%0 Conference Paper %T Inference for multiplicative models %A Ydo Wexler %A Christopher Meek %B Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2008 %E David A. McAllester %E Petri Myllymäki %F pmlr-vR6-wexler08a %I PMLR %P 595--602 %U https://proceedings.mlr.press/r6/wexler08a.html %V R6 %X The paper introduces a generalization for known probabilistic models such as log-linear and graphical models, called here multiplicative models. These models, that express probabilities via product of parameters are shown to capture multiple forms of contextual independence between variables, including decision graphs and noisy-OR functions. An inference algorithm for multiplicative models is provided and its correctness is proved. The complexity analysis of the inference algorithm uses a more refined parameter than the tree-width of the underlying graph, and shows the computational cost does not exceed that of the variable elimination algorithm in graphical models. The paper ends with examples where using the new models and algorithm is computationally beneficial. %Z Reissued by PMLR on 09 October 2024.
APA
Wexler, Y. & Meek, C.. (2008). Inference for multiplicative models. Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R6:595-602 Available from https://proceedings.mlr.press/r6/wexler08a.html. Reissued by PMLR on 09 October 2024.

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