Ebook: Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
- Tags: Statistics and Computing/Statistics Programs, Probability and Statistics in Computer Science, Data Mining and Knowledge Discovery, Artificial Intelligence (incl. Robotics), Operations Research Mathematical Programming, Probability Theor
- Series: Information Science and Statistics
- Year: 2008
- Publisher: Springer New York
- Language: English
- pdf
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. Kj?rulff 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.
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. Kj?rulff 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.
Content:
Front Matter....Pages I-XVII
Introduction....Pages 3-15
Networks....Pages 17-36
Probabilities....Pages 37-62
Probabilistic Networks....Pages 63-106
Solving Probabilistic Networks....Pages 107-139
Eliciting the Model....Pages 143-176
Modeling Techniques....Pages 177-226
Data-Driven Modeling....Pages 227-257
Conflict Analysis....Pages 261-271
Sensitivity Analysis....Pages 273-290
Value of Information Analysis....Pages 291-303
Back Matter....Pages 305-319
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. Kj?rulff 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.
Content:
Front Matter....Pages I-XVII
Introduction....Pages 3-15
Networks....Pages 17-36
Probabilities....Pages 37-62
Probabilistic Networks....Pages 63-106
Solving Probabilistic Networks....Pages 107-139
Eliciting the Model....Pages 143-176
Modeling Techniques....Pages 177-226
Data-Driven Modeling....Pages 227-257
Conflict Analysis....Pages 261-271
Sensitivity Analysis....Pages 273-290
Value of Information Analysis....Pages 291-303
Back Matter....Pages 305-319
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