Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference
2011
Conference Paper
ei
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, 2005) with covariance decoupling techniques (Wipf&Nagarajan, 2008; Nickisch&Seeger, 2009), it runs at least an order of magnitude faster than the most common EP solver.
Author(s): | Seeger, M. and Nickisch, H. |
Book Title: | JMLR Workshop and Conference Proceedings Volume 15: AISTATS 2011 |
Pages: | 652-660 |
Year: | 2011 |
Month: | April |
Day: | 0 |
Editors: | Gordon, G. , D. Dunson, M. Dudík |
Publisher: | MIT Press |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
Event Name: | 14th International Conference on Artificial Intelligence and Statistics |
Event Place: | Fort Lauderdale, FL, USA |
Address: | Cambridge, MA, USA |
Digital: | 0 |
Links: |
PDF
Web |
BibTex @inproceedings{SeegerN2011, title = {Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference }, author = {Seeger, M. and Nickisch, H.}, booktitle = {JMLR Workshop and Conference Proceedings Volume 15: AISTATS 2011}, pages = {652-660}, editors = {Gordon, G. , D. Dunson, M. Dudík }, publisher = {MIT Press}, address = {Cambridge, MA, USA}, month = apr, year = {2011}, doi = {}, month_numeric = {4} } |