Complexity of finding embeddings in a k-tree
SIAM Journal on Algebraic and Discrete Methods
Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A revolution: belief propagation in graphs with cycles
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Bucket elimination: a unifying framework for probabilistic inference
Learning in graphical models
Bayesian networks for pattern classification, data compression, and channel coding
Bayesian networks for pattern classification, data compression, and channel coding
Mini-buckets: a general scheme for generating approximations in automated reasoning
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
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)
Cutset sampling for Bayesian networks
Journal of Artificial Intelligence Research
Branch and bound with mini-bucket heuristics
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Cycle-cutset sampling for Bayesian networks
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Mini-bucket heuristics for improved search
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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It was recently shown that the problem of decoding messages transmitted through a noisy channel can be formulated as a belief updating task over a probabilistic network [14]. Moreover, it was observed that iterative application of the (linear time) belief propagation algorithm designed for polytrees [15] outperformed state of the art decoding algorithms, even though the corresponding networks may have many cycles. This paper demonstrates empirically that an approximation algorithm approx-mpe for solving the most probable explanation (MPE) problem, developed within the recently proposed mini-bucket elimination framework [4], outperforms iterative belief propagation on classes of coding networks that have bounded induced width. Our experiments suggest that approximate MPE decoders can be good competitors to the approximate belief updating decoders.