Empirische Inferenz
Technical Report
2010
Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference
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.
| Author(s): | Seeger, M. and Nickisch, H. |
| Year: | 2010 |
| Month: | December |
| Day: | 0 |
| Bibtex Type: | Technical Report (techreport) |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Institution: | Max Planck Institute for Biological Cybernetics |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
| Links: | |
BibTex
@techreport{6995,
title = {Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference},
abstract = {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.},
organization = {Max-Planck-Gesellschaft},
institution = {Max Planck Institute for Biological Cybernetics},
school = {Biologische Kybernetik},
month = dec,
year = {2010},
slug = {6995},
author = {Seeger, M. and Nickisch, H.},
month_numeric = {12}
}