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Conference Paper

Bayesian Inference for Sparse Generalized Linear Models

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Seeger,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Gerwinn,  S
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83805

Bethge,  M
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://link.springer.com/content/pdf/10.1007%2F978-3-540-74958-5_29.pdf
(Publisher version)

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Citation

Seeger, M., Gerwinn, S., & Bethge, M. (2007). Bayesian Inference for Sparse Generalized Linear Models. In N. Kok, J. Koronacki, R. Lopez de Mantaras, S. Matwin, D. Mladenic, & A. Skowron (Eds.), Machine Learning: ECML 2007: 18th European Conference on Machine Learning, Warsaw, Poland, September 17-21, 2007 (pp. 298-309). Berlin, Germany: Springer.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-CBD5-B
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.