Structure Learning of Probabilistic Graphical Models: A Comprehensive Survey

TL;DR

This paper provides a comprehensive survey of structure learning algorithms for probabilistic graphical models, highlighting their properties, applications, and the challenges involved in learning their structures.

Contribution

It offers an extensive overview of existing structure learning methods for graphical models, emphasizing their significance and the open challenges in the field.

Findings

Summarizes various structure learning algorithms.

Highlights the complexity of structure learning.

Discusses applications across multiple fields.

Abstract

Probabilistic graphical models combine the graph theory and probability theory to give a multivariate statistical modeling. They provide a unified description of uncertainty using probability and complexity using the graphical model. Especially, graphical models provide the following several useful properties: - Graphical models provide a simple and intuitive interpretation of the structures of probabilistic models. On the other hand, they can be used to design and motivate new models. - Graphical models provide additional insights into the properties of the model, including the conditional independence properties. - Complex computations which are required to perform inference and learning in sophisticated models can be expressed in terms of graphical manipulations, in which the underlying mathematical expressions are carried along implicitly. The graphical models have been applied to a large number of fields, including bioinformatics, social science, control theory, image processing, marketing analysis, among others. However, structure learning for graphical models remains an open challenge, since one must cope with a combinatorial search over the space of all possible structures. In this paper, we present a comprehensive survey of the existing structure learning algorithms.