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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
Real-World Appl ications of Bayesian Networks 159
All the queries illustrated above can be easily changed to maximum a posteri-
ori queries by finding the largest element (with which.max) in the distribution
of the target node.
> names(which.max(querygrain(jlow, nodes = c("PKA"))$PKA))
[1] "LOW"
Clearly, such a simple approach is possible because of the nature of the
evidence and the small number of nodes we are exploring. When many nodes
are explored simultaneously, inference on their joint conditional distribution
quickly becomes very difficult and computationally expensive. In these high-
dimensional settings, algorithms specifically designed for MAP
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Publisher Resources

ISBN: 9781482225587