Probability Learning and Soft Quantization in Bayesian Factor Graphs

We focus on learning the probability matrix for discrete random variables in factor graphs. We review the problem and its variational approximation and, via entropic priors, we show that soft quantization can be included in a probabilistically-consistent fashion in a factor graph that learns the mutual relationship among the variables involved. The framework is explained with reference the ”Tipper” example and the results of a Matlab simulation are included.

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