Suitable for graduate students and non-statisticians, this text introduces Bayesian networks using a hands-on approach with simple yet meaningful examples in R illustrating each step of the modeling process. The book explains the whole process of Bayesian network modeling, from structure learning to parameter learning to inference. It also gives a concise but rigorous treatment of the fundamentals of Bayesian networks, offers an introduction to causal Bayesian networks, and evaluates real-world examples involving causal ...
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Suitable for graduate students and non-statisticians, this text introduces Bayesian networks using a hands-on approach with simple yet meaningful examples in R illustrating each step of the modeling process. The book explains the whole process of Bayesian network modeling, from structure learning to parameter learning to inference. It also gives a concise but rigorous treatment of the fundamentals of Bayesian networks, offers an introduction to causal Bayesian networks, and evaluates real-world examples involving causal protein signaling and body composition prediction.
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