## Bayesian Networks: An IntroductionBayesian 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 learning algorithms and Conditional Gaussian Distributions using Junction Tree methods.
- A discussion of Pearl's intervention calculus, with an introduction to the notion of see and do conditioning.
- All concepts are clearly defined and illustrated with examples and exercises. Solutions are provided online.
This book will prove a valuable resource for postgraduate students of statistics, computer engineering, mathematics, data mining, artificial intelligence, and biology. Researchers and users of comparable modelling or statistical techniques such as neural networks will also find this book of interest. |

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algorithm Analysis Applications Bayes Bayesian network causal chain graph ChanDarwiche distance cliques collider connection computed conditional independence conditional independence statements conditional probability potentials configuration Consider defined Definition density function directed acyclic graph directed edges Dirichlet Dirichlet density discrete variables domain dseparated elimination sequence Equation essential graph event example exponential family factor graph finite given in Figure gives graph structure inference instantiated inthe Jeffrey’s rule joint probability junction tree KullbackLeibler divergence Lemma Let denote likelihood marginalization Markov chain Markov equivalent Markov property Mathematics matrix method minimal Monte Carlo moral graph multivariate neighbours node notation ofthe parameter parent Pearl’s prior distribution probabilistic probability distribution probability function problem random variables random vector result Sactive satisfies set of variables simplicial node soft evidence space Statistical subset sufficient statistic Suppose Theorem tothe trail triangulated undirected graph update virtual evidence