Marco Scutari and Jean‐Baptiste Denis. Bayesian Networks: With Examples in R. Boca Raton, CRC Press.

Marco Scutari and Jean‐Baptiste Denis. Bayesian Networks: With Examples in R. Boca Raton, CRC... Bayesian networks (BNs) have seen as varied applications as the broader subject Statistics itself and the research activity continues at full throttle over the past and current decade. The string search “Bayesian Network” at www.scholar.google.com for the periods 1981–90, 1991–2000, 2001–2010, 2011–2017, respectively, yields 196, 3720, 25,800, and 22,200 results. Though the results do not necessarily translate into research articles published on the topic, it is a fairly good indication of the interest that has been evolving on the topic of BNs. It is an area of interest to both the Statistics and Machine Learning community and it has witnessed alike contributions from the two communities. With R as the popular software choice, the current book makes a compelling reading for anybody engaging with these networks. Both the authors have among themselves created some of the most useful R packages for developing the networks.A BN is a graphical representation of nodes, denoting random variables, with conditional dependencies represented through uni‐directional arcs. Some restrictions are placed on the network structure, such as there should be no loops, leading to the famous directed acyclic graph (DAG). The simplest example of a DAG is when we try to model whether the grass http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biometrics Wiley

Marco Scutari and Jean‐Baptiste Denis. Bayesian Networks: With Examples in R. Boca Raton, CRC Press.

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
© 2018, The International Biometric Society
ISSN
0006-341X
eISSN
1541-0420
D.O.I.
10.1111/biom.12856
Publisher site
See Article on Publisher Site

Abstract

Bayesian networks (BNs) have seen as varied applications as the broader subject Statistics itself and the research activity continues at full throttle over the past and current decade. The string search “Bayesian Network” at www.scholar.google.com for the periods 1981–90, 1991–2000, 2001–2010, 2011–2017, respectively, yields 196, 3720, 25,800, and 22,200 results. Though the results do not necessarily translate into research articles published on the topic, it is a fairly good indication of the interest that has been evolving on the topic of BNs. It is an area of interest to both the Statistics and Machine Learning community and it has witnessed alike contributions from the two communities. With R as the popular software choice, the current book makes a compelling reading for anybody engaging with these networks. Both the authors have among themselves created some of the most useful R packages for developing the networks.A BN is a graphical representation of nodes, denoting random variables, with conditional dependencies represented through uni‐directional arcs. Some restrictions are placed on the network structure, such as there should be no loops, leading to the famous directed acyclic graph (DAG). The simplest example of a DAG is when we try to model whether the grass

Journal

BiometricsWiley

Published: Jan 1, 2018

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