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Innovations in Bayesian Networks [electronic resource] : Theory and Applications / edited by Dawn E. Holmes, Lakhmi C. Jain.

By: Holmes, Dawn E [editor.]Contributor(s): Jain, Lakhmi C [editor.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Studies in Computational Intelligence: 156Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008Description: online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540850663Subject(s): Engineering | Artificial intelligence | Engineering mathematics | Engineering | Appl.Mathematics/Computational Methods of Engineering | Artificial Intelligence (incl. Robotics)Additional physical formats: Printed edition:: No titleDDC classification: 519 LOC classification: TA329-348TA640-643Online resources: Click here to access online
Contents:
to Bayesian Networks -- A Polemic for Bayesian Statistics -- A Tutorial on Learning with Bayesian Networks -- The Causal Interpretation of Bayesian Networks -- An Introduction to Bayesian Networks and Their Contemporary Applications -- Objective Bayesian Nets for Systems Modelling and Prognosis in Breast Cancer -- Modeling the Temporal Trend of the Daily Severity of an Outbreak Using Bayesian Networks -- An Information-Geometric Approach to Learning Bayesian Network Topologies from Data -- Causal Graphical Models with Latent Variables: Learning and Inference -- Use of Explanation Trees to Describe the State Space of a Probabilistic-Based Abduction Problem -- Toward a Generalized Bayesian Network -- A Survey of First-Order Probabilistic Models.
In: Springer eBooksSummary: Bayesian networks currently provide one of the most rapidly growing areas of research in computer science and statistics. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. Each of the twelve chapters is self-contained. Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Graduate students since it shows the direction of current research.
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to Bayesian Networks -- A Polemic for Bayesian Statistics -- A Tutorial on Learning with Bayesian Networks -- The Causal Interpretation of Bayesian Networks -- An Introduction to Bayesian Networks and Their Contemporary Applications -- Objective Bayesian Nets for Systems Modelling and Prognosis in Breast Cancer -- Modeling the Temporal Trend of the Daily Severity of an Outbreak Using Bayesian Networks -- An Information-Geometric Approach to Learning Bayesian Network Topologies from Data -- Causal Graphical Models with Latent Variables: Learning and Inference -- Use of Explanation Trees to Describe the State Space of a Probabilistic-Based Abduction Problem -- Toward a Generalized Bayesian Network -- A Survey of First-Order Probabilistic Models.

Bayesian networks currently provide one of the most rapidly growing areas of research in computer science and statistics. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. Each of the twelve chapters is self-contained. Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Graduate students since it shows the direction of current research.

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