Sciweavers

Share
NIPS
2008

Posterior Consistency of the Silverman g-prior in Bayesian Model Choice

9 years 12 months ago
Posterior Consistency of the Silverman g-prior in Bayesian Model Choice
Kernel supervised learning methods can be unified by utilizing the tools from regularization theory. The duality between regularization and prior leads to interpreting regularization methods in terms of maximum a posteriori estimation and has motivated Bayesian interpretations of kernel methods. In this paper we pursue a Bayesian interpretation of sparsity in the kernel setting by making use of a mixture of a point-mass distribution and prior that we refer to as "Silverman's g-prior." We provide a theoretical analysis of the posterior consistency of a Bayesian model choice procedure based on this prior. We also establish the asymptotic relationship between this procedure and the Bayesian information criterion.
Zhihua Zhang, Michael I. Jordan, Dit-Yan Yeung
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2008
Where NIPS
Authors Zhihua Zhang, Michael I. Jordan, Dit-Yan Yeung
Comments (0)
books