t-divergence Based Approximate Inference
Nan Ding ⋅ S.V.N. Vishwanathan ⋅ Yuan Qi
2011 Poster
Abstract
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.
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