The Kernel Beta Process
published: Jan. 16, 2013, recorded: December 2012, views: 3103
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.
For handling large-scale problems, methods like Gaussian processes can be computationally challenging. In this paper, we discuss how use of alternative kernel methods can be employed to accelerate computations, without loss of modeling power. We examine this in the context of general nonparametric Bayesian models, with specific applications within the Beta process. The theoretical and algorithmic issues are discussed, with demonstration via several examples.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !