Bayesian Networks And Decision Graphs
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Bayesian Networks And Decision Graphs
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Author : Thomas Dyhre Nielsen
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-03-17
Bayesian Networks And Decision Graphs written by Thomas Dyhre Nielsen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-03-17 with Science categories.
This is a brand new edition of an essential work on Bayesian networks and decision graphs. It is an introduction to probabilistic graphical models including Bayesian networks and influence diagrams. The reader is guided through the two types of frameworks with examples and exercises, which also give instruction on how to build these models. Structured in two parts, the first section focuses on probabilistic graphical models, while the second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision process and partially ordered decision problems.
Bayesian Networks And Decision Graphs
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Author :
language : en
Publisher:
Release Date : 2001
Bayesian Networks And Decision Graphs written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Bayesian statistical decision theory categories.
Bayesian networks and decision graphs are formal graphical languages for representation and communication of decision scenarios requiring reasoning under uncertainty. Their strengths are two-sided. It is easy for humans to construct and to understand them, and when communicated to a computer, they can easily be compiled. Furthermore, handy algorithms are developed for analyses of the models and for providing responses to a wide range of requests such as belief updating, determining optimal strategies, conflict analyses of evidence, and most probable explanation. The book emphasizes both the human and the computer sides. Part I gives a thorough introduction to Bayesian networks as well as decision trees and infulence diagrams, and through examples and exercises, the reader is instructed in building graphical models from domain knowledge. This part is self-contained and it does not require other background than standard secondary school mathematics. Part II is devoted to the presentation of algorithms and complexity issues. This part is also self-contained, but it requires that the reader is familiar with working with texts in the mathematical language. The author also: - provides a well-founded practical introduction to Bayesian networks, decision trees and influence diagrams; - gives several examples and exercises exploiting the computer systems for Bayesian netowrks and influence diagrams; - gives practical advice on constructiong Bayesian networks and influence diagrams from domain knowledge; - embeds decision making into the framework of Bayesian networks; - presents in detail the currently most efficient algorithms for probability updating in Bayesian networks; - discusses a wide range of analyes tools and model requests together with algorithms for calculation of responses; - gives a detailed presentation of the currently most efficient algorithm for solving influence diagrams.
Advances In Bayesian Networks
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Author : José A. Gámez
language : en
Publisher: Springer
Release Date : 2013-06-29
Advances In Bayesian Networks written by José A. Gámez and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-29 with Mathematics categories.
In recent years probabilistic graphical models, especially Bayesian networks and decision graphs, have experienced significant theoretical development within areas such as artificial intelligence and statistics. This carefully edited monograph is a compendium of the most recent advances in the area of probabilistic graphical models such as decision graphs, learning from data and inference. It presents a survey of the state of the art of specific topics of recent interest of Bayesian Networks, including approximate propagation, abductive inferences, decision graphs, and applications of influence. In addition, Advances in Bayesian Networks presents a careful selection of applications of probabilistic graphical models to various fields such as speech recognition, meteorology or information retrieval.
Bayesian Networks And Influence Diagrams A Guide To Construction And Analysis
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Author : Uffe B. Kjærulff
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-12-20
Bayesian Networks And Influence Diagrams A Guide To Construction And Analysis written by Uffe B. Kjærulff and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-12-20 with Computers categories.
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. This book 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. 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.
Bayesian Networks
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Author : Olivier Pourret
language : en
Publisher: John Wiley & Sons
Release Date : 2008-05-05
Bayesian Networks written by Olivier Pourret and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-05-05 with Mathematics categories.
Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are growing in popularity. Their versatility and modelling power is now employed across a variety of fields for the purposes of analysis, simulation, prediction and diagnosis. This book provides a general introduction to Bayesian networks, defining and illustrating the basic concepts with pedagogical examples and twenty real-life case studies drawn from a range of fields including medicine, computing, natural sciences and engineering. Designed to help analysts, engineers, scientists and professionals taking part in complex decision processes to successfully implement Bayesian networks, this book equips readers with proven methods to generate, calibrate, evaluate and validate Bayesian networks. The book: Provides the tools to overcome common practical challenges such as the treatment of missing input data, interaction with experts and decision makers, determination of the optimal granularity and size of the model. Highlights the strengths of Bayesian networks whilst also presenting a discussion of their limitations. Compares Bayesian networks with other modelling techniques such as neural networks, fuzzy logic and fault trees. Describes, for ease of comparison, the main features of the major Bayesian network software packages: Netica, Hugin, Elvira and Discoverer, from the point of view of the user. Offers a historical perspective on the subject and analyses future directions for research. Written by leading experts with practical experience of applying Bayesian networks in finance, banking, medicine, robotics, civil engineering, geology, geography, genetics, forensic science, ecology, and industry, the book has much to offer both practitioners and researchers involved in statistical analysis or modelling in any of these fields.
Bayesian Decision Networks
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Author : Fouad Sabry
language : en
Publisher: One Billion Knowledgeable
Release Date : 2023-07-01
Bayesian Decision Networks written by Fouad Sabry and has been published by One Billion Knowledgeable this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-01 with Computers categories.
What Is Bayesian Decision Networks A Bayesian network is a probabilistic graphical model that depicts a set of variables and their conditional relationships via a directed acyclic graph (DAG). In other words, a Bayesian network is a type of directed acyclic graph. Bayesian networks are perfect for determining the likelihood that any one of multiple possible known causes was the contributing factor in an event that has already taken place and making a prediction based on that likelihood. For instance, the probabilistic links that exist between diseases and symptoms might be represented by a Bayesian network. The network may be used to compute the odds of the presence of a variety of diseases based on the symptoms that are provided. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Bayesian network Chapter 2: Influence diagram Chapter 3: Graphical model Chapter 4: Hidden Markov model Chapter 5: Decision tree Chapter 6: Gibbs sampling Chapter 7: Decision analysis Chapter 8: Value of information Chapter 9: Probabilistic forecasting Chapter 10: Causal graph (II) Answering the public top questions about bayesian decision networks. (III) Real world examples for the usage of bayesian decision networks in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of bayesian decision networks' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of bayesian decision networks.
Probabilistic Networks And Expert Systems
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Author : Robert G. Cowell
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-07-16
Probabilistic Networks And Expert Systems written by Robert G. Cowell and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-07-16 with Computers categories.
Probabilistic expert systems are graphical networks which support the modeling of uncertainty and decisions in large complex domains, while retaining ease of calculation. Building on original research by the authors, this book gives a thorough and rigorous mathematical treatment of the underlying ideas, structures, and algorithms. The book will be of interest to researchers in both artificial intelligence and statistics, who desire an introduction to this fascinating and rapidly developing field. The book, winner of the DeGroot Prize 2002, the only book prize in the field of statistics, is new in paperback.
Introduction To Bayesian Networks
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Author : Finn V. Jensen
language : en
Publisher: Springer
Release Date : 1997-08-15
Introduction To Bayesian Networks written by Finn V. Jensen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997-08-15 with Mathematics categories.
Disk contains: Tool for building Bayesian networks -- Library of examples -- Library of proposed solutions to some exercises.
Risk Assessment And Decision Analysis With Bayesian Networks
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Author : Norman Fenton
language : en
Publisher: CRC Press
Release Date : 2018-09-03
Risk Assessment And Decision Analysis With Bayesian Networks written by Norman Fenton and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-03 with Mathematics categories.
Since the first edition of this book published, Bayesian networks have become even more important for applications in a vast array of fields. This second edition includes new material on influence diagrams, learning from data, value of information, cybersecurity, debunking bad statistics, and much more. Focusing on practical real-world problem-solving and model building, as opposed to algorithms and theory, it explains how to incorporate knowledge with data to develop and use (Bayesian) causal models of risk that provide more powerful insights and better decision making than is possible from purely data-driven solutions. Features Provides all tools necessary to build and run realistic Bayesian network models Supplies extensive example models based on real risk assessment problems in a wide range of application domains provided; for example, finance, safety, systems reliability, law, forensics, cybersecurity and more Introduces all necessary mathematics, probability, and statistics as needed Establishes the basics of probability, risk, and building and using Bayesian network models, before going into the detailed applications A dedicated website contains exercises and worked solutions for all chapters along with numerous other resources. The AgenaRisk software contains a model library with executable versions of all of the models in the book. Lecture slides are freely available to accredited academic teachers adopting the book on their course.
Bayesian Decision Analysis
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Author : Jim Q. Smith
language : en
Publisher: Cambridge University Press
Release Date : 2010-09-23
Bayesian Decision Analysis written by Jim Q. Smith and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-09-23 with Mathematics categories.
Bayesian decision analysis supports principled decision making in complex domains. This textbook takes the reader from a formal analysis of simple decision problems to a careful analysis of the sometimes very complex and data rich structures confronted by practitioners. The book contains basic material on subjective probability theory and multi-attribute utility theory, event and decision trees, Bayesian networks, influence diagrams and causal Bayesian networks. The author demonstrates when and how the theory can be successfully applied to a given decision problem, how data can be sampled and expert judgements elicited to support this analysis, and when and how an effective Bayesian decision analysis can be implemented. Evolving from a third-year undergraduate course taught by the author over many years, all of the material in this book will be accessible to a student who has completed introductory courses in probability and mathematical statistics.