http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
1 online resource (viii, 347 pages) : illustrations
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.
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.
Includes bibliographical references (pages 355-341) and index.
Chichester, West Sussex, UK : John Wiley, 2009