Gaussian Graphical Models

Authored by: Marloes Maathuis , Mathias Drton , Steffen Lauritzen , Martin Wainwright

Handbook of Graphical Models

Print publication date:  November  2018
Online publication date:  November  2018

Print ISBN: 9781498788625
eBook ISBN: 9780429463976
Adobe ISBN:

10.1201/9780429463976-9

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Abstract

After the discussion of discrete graphical models in the preceding Chapter 8, we now turn to the continuous setting and introduce Gaussian graphical models. As we will see in this chapter, assuming Gaussianity leads to a rich geometric structure that can be exploited for parameter estimation. However, Gaussianity is not only assumed for mathematical simplicity. Gaussian distributions are commonly used for modeling continuous phenomena. As a consequence of the central limit theorem, physical quantities that are expected to be the sum of many independent contributions often follow approximately a Gaussian distribution. For example, people’s height is approximately normally distributed; height is believed to be the sum of many independent contributions from various genetic and environmental factors.

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