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
A
Graph Theory
A.1 Graphs, Nodes and Arcs
A graph G = (V, A) consists of a non-empty set V of nodes or vertices and a
finite (but possibly empty) set A of pairs of vertices called arcs, links or edges.
Each arc a = (u, v) can be defined either as an ordered or an unordered
pair of nodes, which are said to be connected by and incident on the arc and
to be adjacent to each other. Here we will restrict ourselves to graphs having
zero or one connection between any pair of nodes. Since they are adjacent, u
and v are also said to be neighbours. If (u, v) is an ordered pair, u is said to
be the tail of the arc and v the head; then the arc is said to be directe ...
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