Bayesian experimental design
published: Oct. 6, 2014, recorded: December 2013, views: 1866
Download slides: nipsworkshops2013_seeger_experimental_design_01.pdf (3.4 MB)
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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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