Bayesian networks and decision graphs

* Provides a practical introduction to Bayesian networks, object-oriented Bayesian networks, decision trees, influence diagrams and Markov decision processes, making it ideal for both text book and self-study purposes * Step-by-step guides to the construction of Bayesian networks, decision trees, and influence diagrams from domain knowledge, enabling students to recreate the processes for themselves * A thorough introduction to state-of-the-art solution and analysis algorithms, crucial for practical study of the subject Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis.

The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams. The reader is introduced to the two types of frameworks through examples and exercises, which also instruct the reader on how to build these models.