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Author: Norman Fenton Publisher: CRC Press ISBN: 1439809100 Category : Business & Economics Languages : en Pages : 527
Book Description
Although many Bayesian Network (BN) applications are now in everyday use, BNs have not yet achieved mainstream penetration. Focusing on practical real-world problem solving and model building, as opposed to algorithms and theory, Risk Assessment and Decision Analysis with Bayesian Networks explains how to incorporate knowledge with data to develop and use (Bayesian) causal models of risk that provide powerful insights and better decision making. 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, and more Introduces all necessary mathematics, probability, and statistics as needed The book first establishes the basics of probability, risk, and building and using BN models, then goes into the detailed applications. The underlying BN algorithms appear in appendices rather than the main text since there is no need to understand them to build and use BN models. Keeping the body of the text free of intimidating mathematics, the book provides pragmatic advice about model building to ensure models are built efficiently. A dedicated website, www.BayesianRisk.com, contains executable versions of all of the models described, exercises and worked solutions for all chapters, PowerPoint slides, numerous other resources, and a free downloadable copy of the AgenaRisk software.
Author: Norman Fenton Publisher: CRC Press ISBN: 1439809100 Category : Business & Economics Languages : en Pages : 527
Book Description
Although many Bayesian Network (BN) applications are now in everyday use, BNs have not yet achieved mainstream penetration. Focusing on practical real-world problem solving and model building, as opposed to algorithms and theory, Risk Assessment and Decision Analysis with Bayesian Networks explains how to incorporate knowledge with data to develop and use (Bayesian) causal models of risk that provide powerful insights and better decision making. 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, and more Introduces all necessary mathematics, probability, and statistics as needed The book first establishes the basics of probability, risk, and building and using BN models, then goes into the detailed applications. The underlying BN algorithms appear in appendices rather than the main text since there is no need to understand them to build and use BN models. Keeping the body of the text free of intimidating mathematics, the book provides pragmatic advice about model building to ensure models are built efficiently. A dedicated website, www.BayesianRisk.com, contains executable versions of all of the models described, exercises and worked solutions for all chapters, PowerPoint slides, numerous other resources, and a free downloadable copy of the AgenaRisk software.
Author: Norman Fenton Publisher: CRC Press ISBN: 1351978977 Category : Mathematics Languages : en Pages : 661
Book Description
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.
Author: Jim Q. Smith Publisher: Cambridge University Press ISBN: 1139491113 Category : Mathematics Languages : en Pages : 349
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: Norman Fenton Publisher: CRC Press ISBN: 1351978969 Category : Mathematics Languages : en Pages : 783
Book Description
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.
Author: Richard E. Neapolitan Publisher: Prentice Hall ISBN: Category : Computers Languages : en Pages : 704
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: Terje Aven Publisher: John Wiley & Sons ISBN: 0470871237 Category : Mathematics Languages : en Pages : 208
Book Description
Everyday we face decisions that carry an element of risk and uncertainty. The ability to analyse, communicate and control the level of risk entailed by these decisions remains one of the most pressing challenges to the analyst, scientist and manager. This book presents the foundational issues in risk analysis ? expressing risk, understanding what risk means, building risk models, addressing uncertainty, and applying probability models to real problems. The principal aim of the book is to give the reader the knowledge and basic thinking they require to approach risk and uncertainty to support decision making. Presents a statistical framework for dealing with risk and uncertainty. Includes detailed coverage of building and applying risk models and methods. Offers new perspectives on risk, risk assessment and the use of parametric probability models. Highlights a number of applications from business and industry. Adopts a conceptual approach based on elementary probability calculus and statistical theory. Foundations of Risk Analysis provides a framework for understanding, conducting and using risk analysis suitable for advanced undergraduates, graduates, analysts and researchers from statistics, engineering, finance, medicine and the physical sciences, as well as for managers facing decision making problems involving risk and uncertainty.
Author: Olivier Pourret Publisher: John Wiley & Sons ISBN: 9780470994542 Category : Mathematics Languages : en Pages : 446
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: Anca M. Hanea Publisher: Springer Nature ISBN: 3030464741 Category : Business & Economics Languages : en Pages : 503
Book Description
This book pulls together many perspectives on the theory, methods and practice of drawing judgments from panels of experts in assessing risks and making decisions in complex circumstances. The book is divided into four parts: Structured Expert Judgment (SEJ) current research fronts; the contributions of Roger Cooke and the Classical Model he developed; process, procedures and education; and applications. After an Introduction by the Editors, the first part presents chapters on expert elicitation of parameters of multinomial models; the advantages of using performance weighting by advancing the “random expert” hypothesis; expert elicitation for specific graphical models; modelling dependencies between experts’ assessments within a Bayesian framework; preventive maintenance optimization in a Bayesian framework; eliciting life time distributions to parametrize a Dirichlet process; and on an adversarial risk analysis approach for structured expert judgment studies. The second part includes Roger Cooke’s oration from 1995 on taking up his chair at Delft University of Technology; one of the editors reflections on the early decade of the Classical Model development and use; a current overview of the theory of the Classical Model, providing a deep and comprehensive perspective on its foundations and its application; and an interview with Roger Cooke. The third part starts with an interview with Professor Dame Anne Glover, who served as the Chief Scientific Advisor to the President of the European Commission. It then presents chapters on the characteristics of good elicitations by reviewing those advocated and applied; the design and development of a training course for SEJ; and on specific experiences with SEJ protocols with the intention of presenting the challenges and insights collected during these journeys. Finally, the fourth (and largest) part begins with some reflections from Willy Aspinall on his many experiences in applying the Classical Model in several application domains; it continues with related reflections on imperfect elicitations; and then it presents chapters with applications on medicines policy and management, supply chain cyber risk management, geo-political risks, terrorism and the risks facing businesses looking to internationalise.
Author: Jacek Malczewski Publisher: John Wiley & Sons ISBN: 9780471329442 Category : Science Languages : en Pages : 414
Book Description
From selecting sites for new hospitals, schools, and factories, to managing forests and rivers, to creating and maintaining highways and bridges, public and private organizations are often called on to make decisions on geographic questions that involve a multitude of alternatives and often conflicting evaluation criteria. This book presents a formal mechanism for dealing with these situations, capturing the information in a Geographic Information System and processing it to derive optimal recommendations for confronting these complex questions.
Author: Sofia Dias Publisher: John Wiley & Sons ISBN: 1118647505 Category : Mathematics Languages : en Pages : 484
Book Description
A practical guide to network meta-analysis with examples and code In the evaluation of healthcare, rigorous methods of quantitative assessment are necessary to establish which interventions are effective and cost-effective. Often a single study will not provide the answers and it is desirable to synthesise evidence from multiple sources, usually randomised controlled trials. This book takes an approach to evidence synthesis that is specifically intended for decision making when there are two or more treatment alternatives being evaluated, and assumes that the purpose of every synthesis is to answer the question "for this pre-identified population of patients, which treatment is 'best'?" A comprehensive, coherent framework for network meta-analysis (mixed treatment comparisons) is adopted and estimated using Bayesian Markov Chain Monte Carlo methods implemented in the freely available software WinBUGS. Each chapter contains worked examples, exercises, solutions and code that may be adapted by readers to apply to their own analyses. This book can be used as an introduction to evidence synthesis and network meta-analysis, its key properties and policy implications. Examples and advanced methods are also presented for the more experienced reader. Methods used throughout this book can be applied consistently: model critique and checking for evidence consistency are emphasised. Methods are based on technical support documents produced for NICE Decision Support Unit, which support the NICE Methods of Technology Appraisal. Code presented is also the basis for the code used by the ISPOR Task Force on Indirect Comparisons. Includes extensive carefully worked examples, with thorough explanations of how to set out data for use in WinBUGS and how to interpret the output. Network Meta-Analysis for Decision Making will be of interest to decision makers, medical statisticians, health economists, and anyone involved in Health Technology Assessment including the pharmaceutical industry.
Author: Norman Fenton
Author: Norman Fenton
Author: Norman Fenton
Author: Jim Q. Smith
Author: Norman Fenton
Author: Richard E. Neapolitan
Author: Terje Aven
Author: Olivier Pourret
Author: Anca M. Hanea
Author: Jacek Malczewski
Author: Sofia Dias