Efficient algorithms for combinatorial problems on graphs with bounded, decomposability—a survey
BIT - Ellis Horwood series in artificial intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
On the hardness of approximate reasoning
Artificial Intelligence
Simulation Approaches to General Probabilistic Inference on Belief Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Belief Optimization for Binary Networks: A Stable Alternative to Loopy Belief Propagation
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
A General Scheme for Multiple Lower Bound Computation in Constraint Optimization
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
A Comparison of Structural CSP Decomposition Methods
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Approximation algorithms for graphical models
Approximation algorithms for graphical models
Journal of Artificial Intelligence Research
A general algorithm for approximate inference and its application to hybrid bayes nets
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A scheme for approximating probabilistic inference
UAI'97 Proceedings of the Thirteenth 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
Turbo decoding as an instance of Pearl's “belief propagation” algorithm
IEEE Journal on Selected Areas in Communications
Mini-buckets: A general scheme for bounded inference
Journal of the ACM (JACM)
Semiring induced valuation algebras: Exact and approximate local computation algorithms
Artificial Intelligence
Join-graph propagation algorithms
Journal of Artificial Intelligence Research
Iterative join-graph propagation
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Loopy belief propagation as a basis for communication in sensor networks
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Systematic vs. non-systematic algorithms for solving the MPE task
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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The paper presents a parameterized approximation scheme for probabilistic inference. The scheme, called Mini-Clustering (MC), extends the partition-based approximation offered by mini-bucket elimination, to tree decompositions. The benefit of this extension is that all single-variable beliefs are computed (approximately) at once, using a two-phase message-passing process along the cluster tree. The resulting approximation scheme allows adjustable levels of accuracy and efficiency, in anytime style. Empirical evaluation against competing algorithms such as iterative belief propagation and Gibbs sampling demonstrates the potential of the MC approximation scheme for several classes of problems.