NIPS Proceedingsβ

t-divergence Based Approximate Inference

Part of: Advances in Neural Information Processing Systems 24 (NIPS 2011)

[PDF] [BibTeX] [Supplemental]



Approximate inference is an important technique for dealing with large, intractable graphical models based on the exponential family of distributions. We extend the idea of approximate inference to the t-exponential family by defining a new t-divergence. This divergence measure is obtained via convex duality between the log-partition function of the t-exponential family and a new t-entropy. We illustrate our approach on the Bayes Point Machine with a Student's t-prior.