Bayesian experimental design

author: Matthias W. Seeger, Laboratory for Probabilistic Machine Learning, École Polytechnique Fédérale de Lausanne
published: Oct. 6, 2014,   recorded: December 2013,   views: 71
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Description

I will give a brief introduction to sequential Bayesian experimental design, in the sense of greedy maximization of information gain. I will motivate the challenges this program places on approximate Bayesian inference, if it is to be used for high-dimensional signal acquisition optimization. I will outline a framework for variational Bayesian inference in large sparse linear models, with which BED can be implemented for such scenarios.

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