Bayesian networks : an introduction / Timo Koski, John M. Noble.

Includes bibliographical references (pages 355-341) and index.

Graphical models and probabilistic reasoning -- Conditional independence, graphs, and d-separation -- Evidence, sufficiency and Monte Carlo methods -- Decomposable graphs and chain graphs -- Learning the conditional probability potentials -- Learning the graph structure -- Parameters and sensitivity -- Graphical models and exponential families -- Causality and intervention calculus -- The junction tree and probability updating -- Factor graphs and the sum product algorithm.

Bayesian Networks: An Introduction provides a self-contained introduction to the theory and applications of Bayesian networks, a topic of interest and importance for statisticians, computer scientists and those involved in modelling complex data sets. The material has been extensively tested in classroom teaching and assumes a basic knowledge of probability, statistics and mathematics. All notions are carefully explained and feature exercises throughout. Features include:.: An introduction to Dirichlet Distribution, Exponential Families and their applications.; A detailed description of learni.

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