Anytime Marginal MAP Inference


Autoria(s): Mauá, D. D.; de Campos, C. P.
Data(s)

2012

Resumo

This paper presents a new anytime algorithm for the marginal MAP problem in graphical models of bounded treewidth. We show asymptotic convergence and theoretical error bounds for any fixed step. Experiments show that it compares well to a state-of-the-art systematic search algorithm.

Identificador

http://pure.qub.ac.uk/portal/en/publications/anytime-marginal-map-inference(fd0a82ab-4bba-4d1f-837c-1cfa1f596154).html

Idioma(s)

eng

Publicador

Omnipress

Direitos

info:eu-repo/semantics/restrictedAccess

Fonte

Mauá , D D & de Campos , C P 2012 , Anytime Marginal MAP Inference . in International Conference on Machine Learning (ICML) . Omnipress , New York, NY, USA , pp. 1471-1478 .

Tipo

contributionToPeriodical