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 ... Read More
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. Read Less
