Skip to content

Latest commit

 

History

History
46 lines (46 loc) · 1.64 KB

2008-07-09-wexler08a.md

File metadata and controls

46 lines (46 loc) · 1.64 KB
abstract title year layout series publisher issn id month tex_title firstpage lastpage page order cycles bibtex_author author date note address container-title volume genre issued pdf extras
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.
Inference for multiplicative models
2008
inproceedings
Proceedings of Machine Learning Research
PMLR
2640-3498
wexler08a
0
Inference for multiplicative models
595
602
595-602
595
false
Wexler, Ydo and Meek, Christopher
given family
Ydo
Wexler
given family
Christopher
Meek
2008-07-09
Reissued by PMLR on 09 October 2024.
Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence
R6
inproceedings
date-parts
2008
7
9