t-divergence Based Approximate Inference

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

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Nan Ding, Yuan Qi, S.v.n. Vishwanathan


<p>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.</p>