2008-07-09-yu08a.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
Refractor importance sampling	In this paper we introduce Refractor Importance Sampling (RIS), an improvement to reduce error variance in Bayesian network importance sampling propagation under evidential reasoning. We prove the existence of a collection of importance functions that are close to the optimal importance function under evidential reasoning. Based on this theoretic result we derive the RIS algorithm. RIS approaches the optimal importance function by applying localized arc changes to minimize the divergence between the evidence-adjusted importance function and the optimal importance function. The validity and performance of RIS is empirically tested with a large set of synthetic Bayesian networks and two real-world networks.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	yu08a	0	Refractor importance sampling	603	609	603-609	603	false	McAllester, David A. and Myllym{"a}ki, Petri	given family David A. McAllester given family Petri Myllymäki	Yu, Haohai and van Engelen, Robert A.	given family Haohai Yu given family Robert A. van Engelen	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/yu08a/yu08a.pdf