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 (Chapman & Hall to cart. $35.66, good condition, Sold by HPB-Red rated 5.0 out of 5 stars, ships from Dallas, TX, UNITED STATES, published 2014 by Chapman and Hall/CRC.
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