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Introductory and Beginning

Bayesian Networks and Decision Graphs (Information Science and Statistics)
This is a brand new edition of an essential work on Bayesian networks and decision graphs. It is an introduction to probabilistic graphical models including Bayesian networks and influence diagrams. The reader is guided through the two types of frameworks with examples and exercises, which also give instruction on how to build these models. Structured in two parts, the first section focuses on probabilistic graphical models, while the second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision process and partially ordered decision problems.



Modeling and Reasoning with Bayesian Networks

Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning

Causality: Models, Reasoning and Inference

Learning Bayesian Networks

Learning in Graphical Models (Adaptive Computation and Machine Learning)
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