Elements of information theory
Elements of information theory
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Variational Approximations between Mean Field Theory and the Junction Tree Algorithm
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Graph partition strategies for generalized mean field inference
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
A generalized mean field algorithm for variational inference in exponential families
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Focusing generalizations of belief propagation on targeted queries
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Variational upper bounds for probabilistic phylogenetic models
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
Optimization of structured mean field objectives
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Relax, compensate and then recover
JSAI-isAI'10 Proceedings of the 2010 international conference on New Frontiers in Artificial Intelligence
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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.