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Extensively tested in classroom teaching, topics include an introduction to Dirichlet distribution, exponential families, and their applications; a detailed description of learning algorithms and conditional Gaussian distributions using junction tree methods; and a discussion of Pearl's intervention calculus with an introduction to the notion of "see" and "do" conditioning.
Key Topics Covered: * 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 For more information visit http://www.