Artificial Intelligence

    

Directed Dependency Graph Obtained from a Continuous Data Matrix by the Highest Successive Conditionings Method.

Authors: Ait-Taleb Nabil

In this paper, we propose a directed dependency graph learned from a continuous data matrix in order to extract the hidden oriented dependencies from this matrix. For each of the dependency graph's node, we will assign a random variable as well as a conditioning percentage linking parents and children nodes of the graph. Among all the dependency graphs learned from the continuous data matrix, we will choose the one using the highest successive conditionings method.

Comments: 29 Pages.

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Submission history

[v1] 2021-10-08 14:05:29
[v2] 2021-10-20 13:40:03
[v3] 2021-12-23 09:35:56
[v4] 2021-12-30 11:44:46

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