Artificial Intelligence

    

Information Theory Applied to Bayesian Network for Learning Continuous Data Matrix

Authors: Ait-Taleb Nabil

In this paper, we are proposing a learning algorithm for continuous data matrix based on entropy absorption of a Bayesian network.This method consists in losing a little bit of likelihood compared to a chain rule's best likelihood, in order to get a good idea of the higher conditionings that are taking place between the Bayesian network's nodes. We are presenting the known results related to information theory, the multidimensional Gaussian probability, AIC and BIC scores for continuous data matrix learning from a Bayesian network, and we are showing the entropy absorption algorithm using the Kullback-leibler divergence with an example of continuous data matrix.

Comments: 34 Pages.

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Submission history

[v1] 2021-08-08 14:07:27
[v2] 2021-08-22 09:19:01
[v3] 2021-09-16 10:28:57
[v4] 2021-12-28 16:57:48

Unique-IP document downloads: 742 times

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