Learning Mixtures of Discrete Distributions in Recurrent Networks


Learning Mixtures of Discrete Distributions in Recurrent Networks – In this paper we give a systematic analysis of the optimal model selection technique in the literature, with application to the problems of online decision problem formulation and Bayesian inference. A key question to be addressed in this work is to evaluate the model selection technique based on the information theoretic model of learning. In particular, we analyze Bayesian inference based on a general framework of probabilistic inference to learn a posterior conditional model for a given input parameter. The problem of Bayesian inference based on Bayes’ decision problem formulation is posed. We propose an efficient algorithm for Bayesian inference, where the goal is to select the desired model that maximizes the expected posterior distribution. We show that the algorithm is optimal to learn the model, because it is an adaptive selection technique, and so it can learn the posterior conditional model (i.e. of the parameters in the Bayes’ decision problem) that maximizes the expected posterior distribution. We provide theoretical and numerical results using a general model selection problem formulation and show that inference based on Bayes’ decision problem formulation can be efficiently executed in various ways.

This work presents a method allowing an information theoretic system model to extract high-dimensional representations of the data. We provide a principled, efficient algorithm for this task, and a methodology for optimizing the algorithm’s performance. We present a detailed study of the proposed algorithm, which shows that it yields significantly better performance on both synthetic and real data.

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Learning Mixtures of Discrete Distributions in Recurrent Networks

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  • On the effects of conflicting evidence in the course of peer review

    Bayesian Nonparanormal ClusteringThis work presents a method allowing an information theoretic system model to extract high-dimensional representations of the data. We provide a principled, efficient algorithm for this task, and a methodology for optimizing the algorithm’s performance. We present a detailed study of the proposed algorithm, which shows that it yields significantly better performance on both synthetic and real data.


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