Nonparametric belief propagation
Communications of the ACM
Computationally efficient sparse Bayesian learning via belief propagation
IEEE Transactions on Signal Processing
Indoor positioning using nonparametric belief propagation based on spanning trees
EURASIP Journal on Wireless Communications and Networking - Special issue on signal processing-assisted protocols and algorithms for cooperating objects and wireless sensor networks
EURASIP Journal on Wireless Communications and Networking
Large scale probabilistic available bandwidth estimation
Computer Networks: The International Journal of Computer and Telecommunications Networking
Relax, compensate and then recover
JSAI-isAI'10 Proceedings of the 2010 international conference on New Frontiers in Artificial Intelligence
Applications of belief propagation in CSMA wireless networks
IEEE/ACM Transactions on Networking (TON)
The Journal of Machine Learning Research
| Hi-index | 754.85 |
Novel conditions are derived that guarantee convergence of the sum-product algorithm (also known as loopy belief propagation or simply belief propagation (BP)) to a unique fixed point, irrespective of the initial messages, for parallel (synchronous) updates. The computational complexity of the conditions is polynomial in the number of variables. In contrast with previously existing conditions, our results are directly applicable to arbitrary factor graphs (with discrete variables) and are shown to be valid also in the case of factors containing zeros, under some additional conditions. The conditions are compared with existing ones, numerically and, if possible, analytically. For binary variables with pairwise interactions, sufficient conditions are derived that take into account local evidence (i.e., single-variable factors) and the type of pair interactions (attractive or repulsive). It is shown empirically that this bound outperforms existing bounds.