2008-07-09-singliar08a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_editor	editor	bibtex_author	author	date	note	address	container-title	volume	genre	issued	pdf	extras
Efficient inference in persistent Dynamic Bayesian Networks	Numerous temporal inference tasks such as fault monitoring and anomaly detection exhibit a persistence property: for example, if something breaks, it stays broken until an intervention. When modeled as a Dynamic Bayesian Network, persistence adds dependencies between adjacent time slices, often making exact inference over time intractable using standard inference algorithms. However, we show that persistence implies a regular structure that can be exploited for efficient inference. We present three successively more general classes of models: persistent causal chains (PCCs), persistent causal trees (PCTs) and persistent polytrees (PPTs), and the corresponding exact inference algorithms that exploit persistence. We show that analytic asymptotic bounds for our algorithms compare favorably to junction tree inference; and we demonstrate empirically that we can perform exact smoothing on the order of 100 times faster than the approximate Boyen-Koller method on randomly generated instances of persistent tree models. We also show how to handle non-persistent variables and how persistence can be exploited effectively for approximate filtering.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	singliar08a	0	Efficient inference in persistent Dynamic Bayesian Networks	494	502	494-502	494	false	McAllester, David A. and Myllym{"a}ki, Petri	given family David A. McAllester given family Petri Myllymäki	\v{S}ingliar, Tom\'{a}\v{s} and Dash, Denver H.	given family Tomáš Šingliar given family Denver H. Dash	2008-07-09	Reissued by PMLR on 30 October 2024.		Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence	R6	inproceedings	date-parts 2008 7 9	http://proceedings.mlr.press/r6/singliar08a/singliar08a.pdf