Walk-Sums and Belief Propagation in Gaussian Graphical Models
The Journal of Machine Learning Research
Orbit-product representation and correction of Gaussian belief propagation
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Convergence of min-sum message passing for quadratic optimization
IEEE Transactions on Information Theory
Fixing convergence of Gaussian belief propagation
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 3
Convergence of min-sum message-passing for convex optimization
IEEE Transactions on Information Theory
Resource Allocation via Message Passing
INFORMS Journal on Computing
Properties of Bethe free energies and message passing in Gaussian models
Journal of Artificial Intelligence Research
Resource Allocation via Message Passing
INFORMS Journal on Computing
Linear coordinate-descent message passing for quadratic optimization
Neural Computation
| Hi-index | 754.96 |
Motivated by its success in decoding turbo codes, we provide an analysis of the belief propagation algorithm on the turbo decoding graph with Gaussian densities. In this context, we are able to show that, under certain conditions, the algorithm converges and that-somewhat surprisingly-though the density generated by belief propagation may differ significantly from the desired posterior density, the means of these two densities coincide. Since computation of posterior distributions is tractable when densities are Gaussian, use of belief propagation in such a setting may appear unwarranted. Indeed, our primary motivation for studying belief propagation in this context stems from a desire to enhance our understanding of the algorithm's dynamics in a non-Gaussian setting, and to gain insights into its excellent performance in turbo codes. Nevertheless, even when the densities are Gaussian, belief propagation may sometimes provide a more efficient alternative to traditional inference methods