Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference

Details Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference

2010-12-16

arxiv.org/abs/1012.3584v1

Statistics

Machine Learning

16 pages, 3 figures, submitted for conference publication

Scientific paper

We propose a novel algorithm to solve the expectation propagation relaxation of Bayesian inference for continuous-variable graphical models. In contrast to most previous algorithms, our method is provably convergent. By marrying convergent EP ideas from (Opper&Winther 05) with covariance decoupling techniques (Wipf&Nagarajan 08, Nickisch&Seeger 09), it runs at least an order of magnitude faster than the most commonly used EP solver.

Affiliated with

Also associated with

No associations

Rating

Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-219159