Bayesian Network
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Bayesian Networks
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Author : Olivier Pourret
language : en
Publisher: John Wiley & Sons
Release Date : 2008-04-30
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 2008-04-30 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 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.
Modeling And Reasoning With Bayesian Networks
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Author : Adnan Darwiche
language : en
Publisher: Cambridge University Press
Release Date : 2009-04-06
Modeling And Reasoning With Bayesian Networks written by Adnan Darwiche 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 2009-04-06 with Computers categories.
This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.
Bayesian Networks
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Author : Marco Scutari
language : en
Publisher: CRC Press
Release Date : 2021-07-28
Bayesian Networks written by Marco Scutari and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-28 with Computers categories.
Bayesian Networks: With Examples in R, Second Edition introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples illustrate each step of the modelling process and discuss side by side the underlying theory and its application using R code. The examples start from the simplest notions and gradually increase in complexity. In particular, this new edition contains significant new material on topics from modern machine-learning practice: dynamic networks, networks with heterogeneous variables, and model validation. The first three chapters explain the whole process of Bayesian network modelling, from structure learning to parameter learning to inference. These chapters cover discrete, Gaussian, and conditional Gaussian Bayesian networks. The following two chapters delve into dynamic networks (to model temporal data) and into networks including arbitrary random variables (using Stan). The book then gives a concise but rigorous treatment of the fundamentals of Bayesian networks and offers an introduction to causal Bayesian networks. It also presents an overview of R packages and other software implementing Bayesian networks. The final chapter evaluates two real-world examples: a landmark causal protein-signalling network published in Science and a probabilistic graphical model for predicting the composition of different body parts. Covering theoretical and practical aspects of Bayesian networks, this book provides you with an introductory overview of the field. It gives you a clear, practical understanding of the key points behind this modelling approach and, at the same time, it makes you familiar with the most relevant packages used to implement real-world analyses in R. The examples covered in the book span several application fields, data-driven models and expert systems, probabilistic and causal perspectives, thus giving you a starting point to work in a variety of scenarios. Online supplementary materials include the data sets and the code used in the book, which will all be made available from https://www.bnlearn.com/book-crc-2ed/
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.
Learning Bayesian Networks
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Author : Richard E. Neapolitan
language : en
Publisher: Prentice Hall
Release Date : 2004
Learning Bayesian Networks written by Richard E. Neapolitan and has been published by Prentice Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Computers categories.
In this first edition book, methods are discussed for doing inference in Bayesian networks and inference diagrams. Hundreds of examples and problems allow readers to grasp the information. Some of the topics discussed include Pearl's message passing algorithm, Parameter Learning: 2 Alternatives, Parameter Learning r Alternatives, Bayesian Structure Learning, and Constraint-Based Learning. For expert systems developers and decision theorists.
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 : 2012-11-30
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 2012-11-30 with Computers categories.
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition, provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. This new edition contains six new sections, in addition to fully-updated examples, tables, figures, and a revised appendix. 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 or 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.
Bayesian Network
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Author : Ahmed Rebai
language : en
Publisher: BoD – Books on Demand
Release Date : 2010-08-18
Bayesian Network written by Ahmed Rebai and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-08-18 with Mathematics categories.
Bayesian networks are a very general and powerful tool that can be used for a large number of problems involving uncertainty: reasoning, learning, planning and perception. They provide a language that supports efficient algorithms for the automatic construction of expert systems in several different contexts. The range of applications of Bayesian networks currently extends over almost all fields including engineering, biology and medicine, information and communication technologies and finance. This book is a collection of original contributions to the methodology and applications of Bayesian networks. It contains recent developments in the field and illustrates, on a sample of applications, the power of Bayesian networks in dealing the modeling of complex systems. Readers that are not familiar with this tool, but have some technical background, will find in this book all necessary theoretical and practical information on how to use and implement Bayesian networks in their own work. There is no doubt that this book constitutes a valuable resource for engineers, researchers, students and all those who are interested in discovering and experiencing the potential of this major tool of the century.
Bayesian Network
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Author : Fouad Sabry
language : en
Publisher: One Billion Knowledgeable
Release Date : 2024-12-16
Bayesian Network 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 2024-12-16 with Technology & Engineering categories.
1: Bayesian network: Delve into the foundational concepts of Bayesian networks and their applications. 2: Statistical model: Explore the framework of statistical models crucial for data interpretation. 3: Likelihood function: Understand the significance of likelihood functions in probabilistic reasoning. 4: Bayesian inference: Learn how Bayesian inference enhances decisionmaking processes with data. 5: Pattern recognition: Investigate methods for recognizing patterns in complex data sets. 6: Sufficient statistic: Discover how sufficient statistics simplify data analysis while retaining information. 7: Gaussian process: Examine Gaussian processes and their role in modeling uncertainty. 8: Posterior probability: Gain insights into calculating posterior probabilities for informed predictions. 9: Graphical model: Understand the structure and utility of graphical models in representing relationships. 10: Prior probability: Study the importance of prior probabilities in Bayesian reasoning. 11: Gibbs sampling: Learn Gibbs sampling techniques for efficient statistical sampling. 12: Maximum a posteriori estimation: Discover MAP estimation as a method for optimizing Bayesian models. 13: Conditional random field: Explore the use of conditional random fields in structured prediction. 14: Dirichletmultinomial distribution: Understand the Dirichletmultinomial distribution in categorical data analysis. 15: Graphical models for protein structure: Investigate applications of graphical models in bioinformatics. 16: Exponential family random graph models: Delve into exponential family random graphs for network analysis. 17: Bernstein–von Mises theorem: Learn the implications of the Bernstein–von Mises theorem in statistics. 18: Bayesian hierarchical modeling: Explore hierarchical models for analyzing complex data structures. 19: Graphoid: Understand the concept of graphoids and their significance in dependency relations. 20: Dependency network (graphical model): Investigate dependency networks in graphical model frameworks. 21: Probabilistic numerics: Examine probabilistic numerics for enhanced computational methods.
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.