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Bayesian Networks and Influence Diagrams [electronic resource] : A Guide to Construction and Analysis / by Uffe B. Kjærulff, Anders L. Madsen.

By: Kjærulff, Uffe B [author.].
Contributor(s): Madsen, Anders L [author.] | SpringerLink (Online service).
Material type: TextTextSeries: Information Science and Statistics: Publisher: New York, NY : Springer New York, 2008Description: XVIII, 318 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9780387741017.Subject(s): Mathematical statistics | Computer science | Data mining | Artificial intelligence | Distribution (Probability theory | Statistics and Computing/Statistics Programs | Probability and Statistics in Computer Science | Data Mining and Knowledge Discovery | Artificial Intelligence (incl. Robotics) | Operations Research, Management Science | Probability Theory and Stochastic ProcessesAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 519.5 Online resources: Click here to access online
Contents:
Fundamentals -- Networks -- Probabilities -- Probabilistic Networks -- Solving Probabilistic Networks -- Model Construction -- Eliciting the Model -- Modeling Techniques -- Data-Driven Modeling -- Model Analysis -- Conflict Analysis -- Sensitivity Analysis -- Value of Information Analysis.
In: Springer eBooksSummary: Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification, troubleshooting, and data mining under uncertainty. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. 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/her level of understanding. The techniques and methods presented for knowledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined on the basis of numerous courses that 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 holds a PhD on probabilistic networks and is the CEO of HUGIN Expert A/S.
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Fundamentals -- Networks -- Probabilities -- Probabilistic Networks -- Solving Probabilistic Networks -- Model Construction -- Eliciting the Model -- Modeling Techniques -- Data-Driven Modeling -- Model Analysis -- Conflict Analysis -- Sensitivity Analysis -- Value of Information Analysis.

Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification, troubleshooting, and data mining under uncertainty. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. 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/her level of understanding. The techniques and methods presented for knowledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined on the basis of numerous courses that 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 holds a PhD on probabilistic networks and is the CEO of HUGIN Expert A/S.

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