Structured variational methods for distributed inference: convergence analysis and performance-complexity tradeoff

Authors:
Yanbing Zhang;Huaiyu Dai
Affiliations:
Department of Electrical and Computer Engineering, NC State University, Raleigh, N.C.;Department of Electrical and Computer Engineering, NC State University, Raleigh, N.C.
Venue:
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 2
Year:
2009

Citing 6
Cited 0

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
Random graphs

Random graphs
Rapidly Mixing Markov Chains with Applications in Computer Science and Physics

Computing in Science and Engineering
Structured variational methods for distributed inference in wireless ad hoc and sensor networks

ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Randomized gossip algorithms

IEEE Transactions on Information Theory
Consensus Propagation

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, the asymptotic performance of a recently proposed distributed inference framework, structured variational methods, is investigated. We first distinguish the intra- and inter-cluster inference algorithms as vertex and edge processes respectively. Their difference is illustrated, and convergence rate is derived for the intra-cluster inference procedure which is based on an edge process. Then, viewed as a mixed vertex-edge process, the overall performance of structured variational methods is characterized via the coupling approach. Tradeoff between complexity and performance of this algorithm is also addressed, which provides insights for network design and analysis.