A BP-Based Algorithm for Performing Bayesian Inference in Large Networks of Perceptron-Type
Lecture within the tutorial Statistical Methods in Learning

Speaker: Yoshiyuki Kabashima (Tokyo Institute of Technology)

The Bayesian approach provides the optimal performance in many inference problems. However, the necessary cost for computation sometimes prevents it from being practical. In my talk, it is shown that one can develop a practical algorithm to approximately perform the Bayesian inference in large perceptron-type networks by introducing methods and notions of statistical mechanics to Pearl's belief propagation (BP) although the direct application of BP to such problems is computationally difficult. An application of the developed algorithm to a problem that arises in a wireless communication system is also mentioned.

This is joint work with Shinsuke Uda.

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