Artificial Intelligence

    

Bayesian Networks, Kullback-Leibler and Topology

Authors: Ait-Taleb nabil

In this paper, I will propose a topology allowing to measure a neighborhood for the Bayesian networks.This topology will correspond to a Kullback-Leibler distance ratio and will allow to know the distance between a current Bayesian network and a Bayesian network having a chain rule. This topology applied to Bayesian networks will be normalized and will therefore vary from 0 to 1. The value 0 will correspond to a Bayesian network with a chain rule and the value 1 to a Bayesian network without edges.

Comments: 5 Pages.

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Submission history

[v1] 2024-08-30 15:22:54
[v2] 2024-09-11 13:41:25

Unique-IP document downloads: 299 times

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