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ECML
2007
Springer

Bayesian Inference for Sparse Generalized Linear Models

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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.
Matthias Seeger, Sebastian Gerwinn, Matthias Bethg
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where ECML
Authors Matthias Seeger, Sebastian Gerwinn, Matthias Bethge
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