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For a beginner, which is the best book to start with for studying Bayesian Networks?
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Perhaps the most significant disadvantage of an approach involving Bayesian Networks is the fact that there is no universally accepted method for constructing a network from data.
There are two components involved in learning a Bayesian network: (i) structure learning, which involves discovering the DAG that best describes the causal relationships in the data, and (ii) parameter learning, which involves learning about the conditional probability distributions.
In summary, unlike most machine and deep learning methods, Bayesian Networks allow for immediate and direct expert knowledge input. This knowledge is used to control the direction and existence of edges between nodes, therefore encoding knowledge into a directed acyclic graph (DAG).
I would recommend "Probabilistic Graphical Models" by Daphne Koller and Nir Friedman. Its an excellent starter-to-intermediate handbook on both directed (Bayesian Networks) and undirected (Markov Networks) graphical models. The examples given are elaborate and easy to understand.
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A good book on general machine learning is 1. But it is quite light on BN. I haven't read [2] but I have read [3] by him which is good (so, [2] is likely to be good as recommended by dwf). I would not recommend Pearl's book at all unless you are doing your Ph.D.!
However, I actually would recommend the online tutorial "A Brief Introduction to Graphical Models and Bayesian Networks" by Kevin Murphy [4]. The best way to learn BN is to read this, download his Matlab toolbox [5] and build your own BN in ten minutes.
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