Back to Search
Start Over
Belief Propagation for Continuous State Spaces: Stochastic Message-Passing with Quantitative Guarantees.
- Source :
-
Journal of Machine Learning Research . Sep2013, Vol. 14, p2799-2835. 37p. - Publication Year :
- 2013
-
Abstract
- The sum-product or belief propagation (BP) algorithm is a widely used message-passing technique for computing approximate marginals in graphical models. We introduce a new technique, called stochastic orthogonal series message-passing (SOSMP), for computing the BP fixed point in models with continuous random variables. It is based on a deterministic approximation of the messages via orthogonal series basis expansion, and a stochastic estimation of the basis coefficients via Monte Carlo techniques and damped updates. We prove that the SOSMP iterates converge to a d-neighborhood of the unique BP fixed point for any tree-structured graph, and for any graphs with cycles in which the BP updates satisfy a contractivity condition. In addition, we demonstrate how to choose the number of basis coefficients as a function of the desired approximation accuracy d and smoothness of the compatibility functions. We illustrate our theory with both simulated examples and in application to optical flow estimation. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15324435
- Volume :
- 14
- Database :
- Academic Search Index
- Journal :
- Journal of Machine Learning Research
- Publication Type :
- Academic Journal
- Accession number :
- 90728785