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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
42 Bayesian Networks: With Examples in R
nodes to another, which is one way of exploring how the latter depends on the
former. For instance, we can use the path function to look for the existence
of such a path between E and C.
> bnlearn::path(dag.bnlearn, from = "E", to = "C")
[1] TRUE
Note the use of the bnlearn:: prefix to uniquely identify the path func-
tion. A function of the same name is provided by the igraph package, and it
might be called by mistake depending on the order in which the two packages
have been loaded. More operations will be considered in subsequent chapters
as the relevant theory is introduced.
2.3 Probabilistic Representation
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Publisher Resources

ISBN: 9781482225587