Introduction to Graphical Modelling
Machine Learning
2011-06-29 v3 Statistics Theory
Statistics Theory
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.
Cite
@article{arxiv.1005.1036,
title = {Introduction to Graphical Modelling},
author = {Marco Scutari and Korbinian Strimmer},
journal= {arXiv preprint arXiv:1005.1036},
year = {2011}
}
Comments
Handbook of Statistical Systems Biology (D. Balding, M. Stumpf, M. Girolami, eds.), Wiley. 21 pages