A Variational Inference Procedure Allowing Internal Structure for Overlapping Clusters and Deterministic Constraints | Zendy

Dan Geiger | Zendy; Christopher Meek | Zendy; Yonatan Wexler | Zendy

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A Variational Inference Procedure Allowing Internal Structure for Overlapping Clusters and Deterministic Constraints

Author(s) -

Dan Geiger,

Christopher Meek,

Yonatan Wexler

Publication year - 2006

Publication title -

journal of artificial intelligence research

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.79

H-Index - 123

eISSN - 1943-5037

pISSN - 1076-9757

DOI - 10.1613/jair.2028

Subject(s) - inference , computer science , convergence (economics) , algorithm , mathematical optimization , genetic algorithm , theoretical computer science , mathematics , artificial intelligence , machine learning , economics , economic growth

We develop a novel algorithm, called VIP*, for structured variational approximate inference. This algorithm extends known algorithms to allow efficient multiple potential updates for overlapping clusters, and overcomes the difficulties imposed by deterministic constraints. The algorithm's convergence is proven and its applicability demonstrated for genetic linkage analysis.

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