Bayesian experimental design
published: Oct. 6, 2014, recorded: December 2013, views: 75
Download slides: nipsworkshops2013_seeger_experimental_design_01.pdf (3.4 MB)
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !