Model Selection for Graphical Log-linear Models: A Forward Model Selection Algorithm based on Mutual Conditional Independence

TL;DR

This paper introduces a new forward selection algorithm for graphical log-linear models that uses mutual conditional independence checks to efficiently identify the model structure, reducing search space and improving accuracy.

Contribution

The paper proposes a novel forward model selection method based on mutual conditional independence, enhancing structure learning in graphical log-linear models.

Findings

Efficient reduction of search space in model selection.

Effective identification of important interactions.

Successful application to real dataset example.

Abstract

Model selection and learning the structure of graphical models from the data sample constitutes an important field of probabilistic graphical model research, as in most of the situations the structure is unknown and has to be learnt from the given dataset. In this paper, we present a new forward model selection algorithm for graphical log-linear models. We use mutual conditional independence check to reduce the search space which also takes care of the evaluation of the joint effects and chances of missing important interactions are eliminated. We illustrate our algorithm with a real dataset example.