The Factored Frontier Algorithm for Approximate Inference in DBNs Export

@inproceedings{citeulike:757898, abstract = {The Factored Frontier (FF) algorithm is a simple approximate inference algorithm for Dynamic Bayesian Networks (DBNs). It is very similar to the fully factorized version of the Boyen-Koller (BK) algorithm, but instead of doing an exact update at every step followed by marginalisation (projection), it always works with factored distributions. Hence it can be applied to models for which the exact update step is intractable. We show that FF is equivalent to (one iteration of) loopy belief...}, author = {Murphy, Kevin and Weiss, Yair}, citeulike-article-id = {757898}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.6380}, keywords = {algorithm, approximate, dbn, inference}, pages = {378--385}, posted-at = {2006-07-13 18:12:57}, priority = {2}, title = {The Factored Frontier Algorithm for Approximate Inference in DBNs}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.6380} }

TY - CONF ID - citeulike:757898 L3 - citeulike-article-id:757898 N2 - The Factored Frontier (FF) algorithm is a simple approximate inference algorithm for Dynamic Bayesian Networks (DBNs). It is very similar to the fully factorized version of the Boyen-Koller (BK) algorithm, but instead of doing an exact update at every step followed by marginalisation (projection), it always works with factored distributions. Hence it can be applied to models for which the exact update step is intractable. We show that FF is equivalent to (one iteration of) loopy belief... EP - 385 TI - The Factored Frontier Algorithm for Approximate Inference in DBNs SP - 378 KW - algorithm KW - approximate KW - dbn KW - inference AU - Murphy, Kevin AU - Weiss, Yair UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.6380 ER -