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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Add this copy of Bayesian Networks: With Examples in R to cart. $108.03, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2021 by CRC Press.
