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Statistics - Computational Statistics | Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis

Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis

Kjærulff, Uffe B., Madsen, Anders L.

2nd ed. 2013, XVIII, 382 p.

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  • Comprehensive introduction to understand, construct, and analyze probabilistic networks
  • Second Edition features six new sections covering such topics as structure learning and parameter tuning
  • New appendix offers recommendations and solutions to common problems associated with model building
  • Written specifically for practitioners of applied artificial intelligence

Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition, provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. This new edition contains six new sections, in addition to fully-updated examples, tables, figures, and a revised appendix.  Intended primarily for practitioners, this book does not require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning under uncertainty. The theory and methods presented are illustrated through more than 140 examples, and exercises are included for the reader to check his or her level of understanding. The techniques and methods presented on model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined based on numerous courses the authors have held for practitioners worldwide.  

Uffe B. Kjærulff holds a PhD on probabilistic networks and is an Associate Professor of Computer Science at Aalborg University. Anders L. Madsen of HUGIN EXPERT A/S holds a PhD on probabilistic networks and is an Adjunct Professor of Computer Science at Aalborg University.

Content Level » Professional/practitioner

Keywords » Bayesian Networks - Graphical Models - Influence Diagrams - Model Analysis - Model Construction - Probabilistic Networks

Related subjects » Applications - Artificial Intelligence - Computational Statistics - Database Management & Information Retrieval - Probability Theory and Stochastic Processes - Theoretical Computer Science

Table of contents 

Introduction.- Networks.- Probabilities.- Probabilistic Networks.- Solving Probabilistic Networks.- Eliciting the Model.- Modeling Techniques.- Data-Driven Modeling.- Conflict Analysis.- Sensitivity Analysis.- Value of Information Analysis.- Quick Reference to Model Construction.- List of Examples.- List of Figures.- List of Tables.- List of Symbols.- References.- Index.

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