Open AccessBook
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Uffe Kjærulff,Anders L. Madsen +1 more
TLDR
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.Abstract:
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 worldwideread more
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Book
Machine Learning : A Probabilistic Perspective
TL;DR: This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach, and is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
Book
Bayesian Reasoning and Machine Learning
TL;DR: Comprehensive and coherent, this hands-on text develops everything from basic reasoning to advanced techniques within the framework of graphical models, and develops analytical and problem-solving skills that equip them for the real world.
Journal ArticleDOI
Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches
TL;DR: The paper concludes that BN is a superior technique in safety analysis because of its flexible structure, allowing it to fit a wide variety of accident scenarios.
Journal ArticleDOI
Review: learning bayesian networks: Approaches and issues
TL;DR: This work takes a broad look at the literature on learning Bayesian networks—in particular their structure—from data, and hopes that all the major fields in the area are covered.
Journal ArticleDOI
Incorporation of formal safety assessment and Bayesian network in navigational risk estimation of the Yangtze River
TL;DR: The navigational risk model is established by considering both probability and consequences of accidents with respect to a risk matrix method, followed by a scenario analysis to demonstrate the application of the proposed model.
References
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Journal ArticleDOI
The Framing of Decisions and the Psychology of Choice
Amos Tversky,Daniel Kahneman +1 more
TL;DR: The psychological principles that govern the perception of decision problems and the evaluation of probabilities and outcomes produce predictable shifts of preference when the same problem is framed in different ways.
Journal ArticleDOI
Pattern Classification and Scene Analysis.
Book
A mathematical theory of evidence
TL;DR: This book develops an alternative to the additive set functions and the rule of conditioning of the Bayesian theory: set functions that need only be what Choquet called "monotone of order of infinity." and Dempster's rule for combining such set functions.
MonographDOI
Causality: models, reasoning, and inference
TL;DR: The art and science of cause and effect have been studied in the social sciences for a long time as mentioned in this paper, see, e.g., the theory of inferred causation, causal diagrams and the identification of causal effects.
Journal ArticleDOI
Bayesian Network Classifiers
TL;DR: Tree Augmented Naive Bayes (TAN) is single out, which outperforms naive Bayes, yet at the same time maintains the computational simplicity and robustness that characterize naive Baye.