Bayesian Inference for Sparse Generalized Linear Models

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.

• Type: Conference
• Year: 2007
• Where: ECML
• Authors: Matthias Seeger, Sebastian Gerwinn, Matthias Bethge
• Keywords: Approximate Bayesian Inference, Bayesian GLMs, ECML 2007, Machine Learning, Prior Distribution