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.
Keywords. Credal networks, variational methods, inferences
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Authors addresses:
Jaime Ide
Av. Prof. Mello Moraes, 2231
Fabio Cozman
Av. Prof. Mello Moraes, 2231
Cidade Univesitaria, CEP 05508-900
Sao Paulo, SP - BRAZIL
E-mail addresses:
| Jaime Ide | jaime.ide@poli.usp.br |
| Fabio Cozman | fgcozman@usp.br |