We present a framework for efficient, accurate approximate Bayesian inference in generalized linear models (GLMs), based on the expectation propagation (EP) technique. The parameters can be endowed with a factorizing prior distribution, encoding properties such as sparsity or non-negativity. The central role of posterior log-concavity in Bayesian GLMs is emphasized and related to stability issues in EP. In particular, we use our technique to infer the parameters of a point process model for neuronal spiking data from multiple electrodes, demonstrating significantly superior predictive performance when a sparsity assumption is enforced via a Laplace prior distribution.
| Author(s): | Seeger, M. and Gerwinn, S. and Bethge, M. |
| Book Title: | ECML 2007 |
| Journal: | Machine Learning: ECML 2007 |
| Pages: | 298-309 |
| Year: | 2007 |
| Month: | September |
| Day: | 0 |
| Series: | Lecture Notes in Computer Science ; 4701 |
| Editors: | Kok, J. N., J. Koronacki, R. Lopez de Mantaras, S. Matwin, D. Mladenic, A. Skowron |
| Publisher: | Springer |
| Bibtex Type: | Conference Paper (inproceedings) |
| Address: | Berlin, Germany |
| DOI: | 10.1007/978-3-540-74958-5_29 |
| Event Name: | 18th European Conference on Machine Learning |
| Event Place: | Warsaw, Poland |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
| Links: | |
BibTex
@inproceedings{4807,
title = {Bayesian Inference for Sparse Generalized Linear Models},
journal = {Machine Learning: ECML 2007},
booktitle = {ECML 2007},
abstract = {We present a framework for efficient, accurate approximate Bayesian inference in generalized linear models (GLMs), based on the expectation propagation (EP) technique. The parameters can be endowed with a factorizing prior distribution, encoding properties such as sparsity or non-negativity. The central role of posterior log-concavity in Bayesian GLMs is emphasized and related to stability issues in EP. In particular, we use our technique to infer the parameters of a point process model for neuronal spiking data from multiple electrodes, demonstrating significantly superior predictive performance when a sparsity assumption is enforced via a Laplace prior distribution.},
pages = {298-309},
series = {Lecture Notes in Computer Science ; 4701},
editors = {Kok, J. N., J. Koronacki, R. Lopez de Mantaras, S. Matwin, D. Mladenic, A. Skowron},
publisher = {Springer},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
address = {Berlin, Germany},
month = sep,
year = {2007},
slug = {4807},
author = {Seeger, M. and Gerwinn, S. and Bethge, M.},
month_numeric = {9}
}