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
Biometrics – Wiley
Published: Jan 1, 2018
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