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