Anytime marginal maximum a posteriori inference

Denis Deratani Mauá; Cassio Polpo de Campos

Anytime marginal maximum a posteriori inference

Denis Deratani Mauá
, Cassio Polpo de Campos

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

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.

Original language	English
Title of host publication	Proceedings of the 29th International Conference on Machine Learning, ICML 2012
Pages	1471-1478
Number of pages	8
Volume	2
Publication status	Published - 10 Oct 2012
Externally published	Yes
Event	29th International Conference on Machine Learning (ICML 2012) - Edinburgh, United Kingdom Duration: 26 Jun 2012 → 1 Jul 2012 Conference number: 29

Conference

Conference	29th International Conference on Machine Learning (ICML 2012)
Abbreviated title	ICML 2012
Country/Territory	United Kingdom
City	Edinburgh
Period	26/06/12 → 1/07/12

Cite this

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title = "Anytime marginal maximum a posteriori inference",

abstract = "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.",

author = "Mau{\'a}, \{Denis Deratani\} and \{de Campos\}, \{Cassio Polpo\}",

year = "2012",

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AU - Mauá, Denis Deratani

AU - de Campos, Cassio Polpo

N1 - Conference code: 29

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Y1 - 2012/10/10

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/84867124447

M3 - Conference contribution

AN - SCOPUS:84867124447

SN - 9781450312851

VL - 2

SP - 1471

EP - 1478

BT - Proceedings of the 29th International Conference on Machine Learning, ICML 2012

T2 - 29th International Conference on Machine Learning (ICML 2012)

Y2 - 26 June 2012 through 1 July 2012

ER -

Anytime marginal maximum a posteriori inference

Abstract

Conference

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