Approximate inference in credal networks by variational mean field methods

Jaime Shinsuke Ide; Fabio Gagliardi Cozman

Approximate inference in credal networks by variational mean field methods

Jaime Shinsuke Ide, Fabio Gagliardi Cozman

Resultado de la investigación: Contribución a una conferencia › Artículo de conferencia

7 Citas (Scopus)

Resumen

© 2005 Society for Imprecise Probability: Theories and Applications, SIPTA. All rights reserved. Graph-theoretical representations for sets of probability measures (credal networks) generally display high complexity, and approximate inference seems to be a natural solution for large networks. This paper introduces a variational approach to approximate inference in credal networks: we show how to formulate mean field approximations using naive (fully factorized) and structured (tree-like) schemes. We discuss the computational advantages of the variational approach, and present examples that illustrate the mechanics of the proposal.

Idioma original	Inglés estadounidense
Estado	Publicada - 1 ene 2005
Publicado de forma externa	Sí
Evento	4th International Symposium on Imprecise Probabilities and Their Applications, ISIPTA 2005 - Duración: 1 ene 2005 → …

Conferencia

Conferencia	4th International Symposium on Imprecise Probabilities and Their Applications, ISIPTA 2005
Período	1/01/05 → …

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AB - © 2005 Society for Imprecise Probability: Theories and Applications, SIPTA. All rights reserved. Graph-theoretical representations for sets of probability measures (credal networks) generally display high complexity, and approximate inference seems to be a natural solution for large networks. This paper introduces a variational approach to approximate inference in credal networks: we show how to formulate mean field approximations using naive (fully factorized) and structured (tree-like) schemes. We discuss the computational advantages of the variational approach, and present examples that illustrate the mechanics of the proposal.

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Approximate inference in credal networks by variational mean field methods

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