Iterative Multiagent Probabilistic Inference
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Robust message-passing for statistical inference in sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
Estimating the "Wrong" Graphical Model: Benefits in the Computation-Limited Setting
The Journal of Machine Learning Research
Walk-Sums and Belief Propagation in Gaussian Graphical Models
The Journal of Machine Learning Research
Dynamic multiagent probabilistic inference
International Journal of Approximate Reasoning
The Journal of Machine Learning Research
Graphical Models, Exponential Families, and Variational Inference
Foundations and Trends® in Machine Learning
Convexity arguments for efficient minimization of the Bethe and Kikuchi free energies
Journal of Artificial Intelligence Research
Message quantization in belief propagation: structural results in the low-rate regime
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
A low density lattice decoder via non-parametric belief propagation
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Bayesian compressive sensing via belief propagation
IEEE Transactions on Signal Processing
Nonparametric belief propagation
Communications of the ACM
Efficient sequential clamping for lifted message passing
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Applications of gibbs measure theory to loopy belief propagation algorithm
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Applications of belief propagation in CSMA wireless networks
IEEE/ACM Transactions on Networking (TON)
The Journal of Machine Learning Research
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Belief propagation (BP) is an increasingly popular method of performing approximate inference on arbitrary graphical models. At times, even further approximations are required, whether due to quantization of the messages or model parameters, from other simplified message or model representations, or from stochastic approximation methods. The introduction of such errors into the BP message computations has the potential to affect the solution obtained adversely. We analyze the effect resulting from message approximation under two particular measures of error, and show bounds on the accumulation of errors in the system. This analysis leads to convergence conditions for traditional BP message passing, and both strict bounds and estimates of the resulting error in systems of approximate BP message passing.