Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Elements of information theory
Elements of information theory
A revolution: belief propagation in graphs with cycles
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
An Introduction to Variational Methods for Graphical Models
Machine Learning
Mini-buckets: A general scheme for bounded inference
Journal of the ACM (JACM)
Variational Approximations between Mean Field Theory and the Junction Tree Algorithm
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
On the choice of regions for generalized belief propagation
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Survey propagation: An algorithm for satisfiability
Random Structures & Algorithms
An edge deletion semantics for belief propagation and its practical impact on approximation quality
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Journal of Artificial Intelligence Research
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Iterative join-graph propagation
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Bucket elimination: a unifying framework for probabilistic inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
A comparative study of energy minimization methods for markov random fields
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Tree-based reparameterization framework for analysis of sum-product and related algorithms
IEEE Transactions on Information Theory
Constructing free-energy approximations and generalized belief propagation algorithms
IEEE Transactions on Information Theory
Turbo decoding as an instance of Pearl's “belief propagation” algorithm
IEEE Journal on Selected Areas in Communications
Indexing correlated probabilistic databases
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Relax, compensate and then recover: a theory of anytime, approximate inference
JELIA'10 Proceedings of the 12th European conference on Logics 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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A recent formalization of Iterative Belief Propagation (IBP) has shown that it can be understood as an exact inference algorithm on an approximate model that results from deleting every model edge. This formalization has led to (1) new realizations of Generalized Belief Propagation (GBP) in which edges are recovered incrementally to improve approximation quality, and (2) edge-recovery heuristics that are motivated by improving the approximation quality of all node marginals in a graphical model. In this paper, we propose new edge-recovery heuristics, which are focused on improving the approximations of targeted node marginals. The new heuristics are based on newly-identified properties of edge deletion, and in turn IBP, which guarantee the exactness of edge deletion in simple and idealized cases. These properties also suggest new improvements to IBP approximations which are based on performing edge-by-edge corrections on targeted marginals, which are less costly than improvements based on edge recovery.