Skip to Main Content
Bayesian Networks
book

Bayesian Networks

by Marco Scutari, Jean-Baptiste Denis
June 2014
Intermediate to advanced content levelIntermediate to advanced
241 pages
6h 20m
English
CRC Press
Content preview from Bayesian Networks
2
The Continuous Case: Gaussian Bayesian
Networks
In this chapter we will continue our exploration of BNs, fo cusing on modelling
continuous data under a multivariate Normal (Gaussian) assumption.
2.1 Introductory Example: Crop Analysis
Suppose that we are interested in the analysis of a particular plant, which we
will model in a very simplistic way by considering:
the potential of the plant and of the environment;
the production of vegetative mass;
and the harvested grain mass, which is called the crop.
To be more precise, we define two synthetic variables to describe the initial
status of the plant: its genetic potential, which we will denote as ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Practical Applications of Bayesian Reliability

Practical Applications of Bayesian Reliability

Yan Liu, Athula I. Abeyratne
Benefits of Bayesian Network Models

Benefits of Bayesian Network Models

Philippe Weber, Christophe Simon
Learning Bayesian Models with R

Learning Bayesian Models with R

Hari Manassery Koduvely

Publisher Resources

ISBN: 9781482225587