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.


Published in:
European Conference on Machine Learning 2007, 4701, 298-309
Presented at:
European Conference on Machine Learning
Year:
2007
Publisher:
Springer
Keywords:
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 Record created 2010-12-01, last modified 2018-03-17

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