Introduction to Graphical Modelling

TL;DR

This chapter provides an overview of graphical models, covering their mathematical foundations, properties, estimation, inference, and applications in systems biology.

Contribution

It offers a comprehensive introduction to the theory and applications of graphical models, focusing on Markov and Bayesian networks.

Findings

Developed understanding of graphical model properties

Reviewed estimation and inference procedures

Highlighted applications in systems biology

Abstract

The aim of this chapter is twofold. In the first part we will provide a brief overview of the mathematical and statistical foundations of graphical models, along with their fundamental properties, estimation and basic inference procedures. In particular we will develop Markov networks (also known as Markov random fields) and Bayesian networks, which comprise most past and current literature on graphical models. In the second part we will review some applications of graphical models in systems biology.