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Author: Uffe B. Kjærulff
Publisher: Springer Science & Business Media
ISBN: 0387741011
Category : Computers
Languages : en
Pages : 325
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Book Description
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.
Author: Uffe B. Kjærulff
Publisher: Springer Science & Business Media
ISBN: 0387741011
Category : Computers
Languages : en
Pages : 325
Get Book Here
Book Description
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.
Author: Uffe B. Kjærulff
Publisher: Springer Science & Business Media
ISBN: 1461451043
Category : Computers
Languages : en
Pages : 388
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Book Description
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.
Author: Thomas Dyhre Nielsen
Publisher: Springer Science & Business Media
ISBN: 0387682821
Category : Science
Languages : en
Pages : 457
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Book Description
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.
Author: Olivier Pourret
Publisher: John Wiley & Sons
ISBN: 9780470994542
Category : Mathematics
Languages : en
Pages : 446
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Book Description
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.
Author: Jim Q. Smith
Publisher: Cambridge University Press
ISBN: 1139491113
Category : Mathematics
Languages : en
Pages : 349
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Book Description
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.
Author: Duncan Taylor
Publisher: CRC Press
ISBN: 1000801381
Category : Science
Languages : en
Pages : 583
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Book Description
Forensic DNA Trace Evidence Interpretation: Activity Level Propositions and Likelihood Ratios provides all foundational information required for a reader to understand the practice of evaluating forensic biology evidence given activity level propositions and to implement the practice into active casework within a forensic institution. The book begins by explaining basic concepts and foundational theory, pulling together research and studies that have accumulated in forensic journal literature over the last 20 years. The book explains the laws of probability - showing how they can be used to derive, from first principles, the likelihood ratio - used throughout the book to express the strength of evidence for any evaluation. Concepts such as the hierarchy of propositions, the difference between experts working in an investigative or evaluative mode and the practice of case assessment and interpretation are explained to provide the reader with a broad grounding in the topics that are important to understanding evaluation of evidence. Activity level evaluations are discussed in relation to biological material transferred from one object to another, the ability for biological material to persist on an item for a period of time or through an event, the ability to recover the biological material from the object when sampled for forensic testing and the expectations of the prevalence of biological material on objects in our environment. These concepts of transfer, persistence, prevalence and recovery are discussed in detail in addition to the factors that affect each of them. The authors go on to explain the evaluation process: how to structure case information and formulate propositions. This includes how a likelihood ratio formula can be derived to evaluate the forensic findings, introducing Bayesian networks and explaining what they represent and how they can be used in evaluations and showing how evaluation can be tested for robustness. Using these tools, the authors also demonstrate the ways that the methods used in activity level evaluations are applied to questions about body fluids. There are also chapters dedicated to reporting of results and implementation of activity level evaluation in a working forensic laboratory. Throughout the book, four cases are used as examples to demonstrate how to relate the theory to practice and detail how laboratories can integrate and implement activity level evaluation into their active casework.
Author: Wojciech Zamojski
Publisher: Springer
ISBN: 3319914464
Category : Technology & Engineering
Languages : en
Pages : 581
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Book Description
This book presents the proceedings of the Thirteenth International Conference on Dependability and Complex Systems (DepCoS-RELCOMEX), which took place in the Brunów Palace in Poland from 2nd to 6th July 2018. The conference has been organized at the Faculty of Electronics, Wrocław University of Science and Technology since 2006, and it continues the tradition of two other events: RELCOMEX (1977–89) and Microcomputer School (1985–95). The selection of papers in these proceedings illustrates the broad variety of topics that are investigated in dependability analyses of today’s complex systems. Dependability came naturally as a contemporary answer to new challenges in the reliability evaluation of these systems. Such systems cannot be considered only as structures (however complex and distributed) built on the basis of technical resources (hardware): their analysis must take into account a unique blend of interacting people (their needs and behaviours), networks (together with mobile properties, cloud-based systems) and a large number of users dispersed geographically and producing an unimaginable number of applications (working online). A growing number of research methods apply the latest advances in artificial intelligence (AI) and computational intelligence (CI). Today’s complex systems are really complex and are applied in numerous different fields of contemporary life.
Author: Richard E. Neapolitan
Publisher: Prentice Hall
ISBN:
Category : Computers
Languages : en
Pages : 704
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Book Description
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.
Author: Linda C. van der Gaag
Publisher: Springer
ISBN: 3319114336
Category : Computers
Languages : en
Pages : 609
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Book Description
This book constitutes the refereed proceedings of the 7th International Workshop on Probabilistic Graphical Models, PGM 2014, held in Utrecht, The Netherlands, in September 2014. The 38 revised full papers presented in this book were carefully reviewed and selected from 44 submissions. The papers cover all aspects of graphical models for probabilistic reasoning, decision making, and learning.
Author: Yousef Farhaoui
Publisher: Springer
ISBN: 3030236722
Category : Computers
Languages : en
Pages : 380
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Book Description
This book reviews the state of the art in big data analysis and networks technologies. It addresses a range of issues that pertain to: signal processing, probability models, machine learning, data mining, databases, data engineering, pattern recognition, visualization, predictive analytics, data warehousing, data compression, computer programming, smart cities, networks technologies, etc. Data is becoming an increasingly decisive resource in modern societies, economies, and governmental organizations. In turn, data science inspires novel techniques and theories drawn from mathematics, statistics, information theory, computer science, and the social sciences. All papers presented here are the product of extensive field research involving applications and techniques related to data analysis in general, and to big data and networks technologies in particular. Given its scope, the book will appeal to advanced undergraduate and graduate students, postdoctoral researchers, lecturers and industrial researchers, as well general readers interested in big data analysis and networks technologies.
