Mathematics

Risk Assessment and Decision Analysis with Bayesian Networks, Second Edition

Author: Norman Fenton,Martin Neil

Publisher: CRC Press

ISBN: 1351978969

Category: Mathematics

Page: 704

View: 7758

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.
Business & Economics

Risk Assessment and Decision Analysis with Bayesian Networks

Author: Norman Fenton,Martin Neil

Publisher: CRC Press

ISBN: 1439809119

Category: Business & Economics

Page: 524

View: 4882

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.
Business & Economics

Risk Assessment and Decision Analysis with Bayesian Networks

Author: Norman Fenton,Martin Neil

Publisher: CRC Press

ISBN: 1439809100

Category: Business & Economics

Page: 524

View: 7972

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.
Mathematics

Bayesian Networks and Decision Graphs

Author: Thomas Dyhre Nielsen,FINN VERNER JENSEN

Publisher: Springer Science & Business Media

ISBN: 1475735022

Category: Mathematics

Page: 268

View: 8233

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 understand them, and when communicated to a computer, they can easily be compiled. The book emphasizes both the human and the computer side. It gives a thorough introduction to Bayesian networks, decision trees and influence diagrams as well as algorithms and complexity issues.
Mathematics

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science

Author: Franco Taroni,Alex Biedermann,Silvia Bozza,Paolo Garbolino,Colin Aitken

Publisher: John Wiley & Sons

ISBN: 1118914740

Category: Mathematics

Page: 472

View: 4072

"This book should have a place on the bookshelf of every forensic scientist who cares about the science of evidence interpretation" Dr. Ian Evett, Principal Forensic Services Ltd, London, UK Continuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing. The commensurate increase in complexity creates difficulties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision. Probability theory, implemented through graphical methods, and specifically Bayesian networks, provides powerful methods to deal with this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to the judicial system. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science provides a unique and comprehensive introduction to the use of Bayesian decision networks for the evaluation and interpretation of scientific findings in forensic science, and for the support of decision-makers in their scientific and legal tasks. • Includes self-contained introductions to probability and decision theory. • Develops the characteristics of Bayesian networks, object-oriented Bayesian networks and their extension to decision models. • Features implementation of the methodology with reference to commercial and academically available software. • Presents standard networks and their extensions that can be easily implemented and that can assist in the reader’s own analysis of real cases. • Provides a technique for structuring problems and organizing data based on methods and principles of scientific reasoning. • Contains a method for the construction of coherent and defensible arguments for the analysis and evaluation of scientific findings and for decisions based on them. • Is written in a lucid style, suitable for forensic scientists and lawyers with minimal mathematical background. • Includes a foreword by Ian Evett. The clear and accessible style of this second edition makes this book ideal for all forensic scientists, applied statisticians and graduate students wishing to evaluate forensic findings from the perspective of probability and decision analysis. It will also appeal to lawyers and other scientists and professionals interested in the evaluation and interpretation of forensic findings, including decision making based on scientific information.
Business & Economics

Coherent Stress Testing

A Bayesian Approach to the Analysis of Financial Stress

Author: Riccardo Rebonato

Publisher: John Wiley & Sons

ISBN: 0470971487

Category: Business & Economics

Page: 238

View: 1503

In Coherent Stress Testing: A Bayesian Approach, industry expert Riccardo Rebonato presents a groundbreaking new approach to this important but often undervalued part of the risk management toolkit. Based on the author's extensive work, research and presentations in the area, the book fills a gap in quantitative risk management by introducing a new and very intuitively appealing approach to stress testing based on expert judgement and Bayesian networks. It constitutes a radical departure from the traditional statistical methodologies based on Economic Capital or Extreme-Value-Theory approaches. The book is split into four parts. Part I looks at stress testing and at its role in modern risk management. It discusses the distinctions between risk and uncertainty, the different types of probability that are used in risk management today and for which tasks they are best used. Stress testing is positioned as a bridge between the statistical areas where VaR can be effective and the domain of total Keynesian uncertainty. Part II lays down the quantitative foundations for the concepts described in the rest of the book. Part III takes readers through the application of the tools discussed in part II, and introduces two different systematic approaches to obtaining a coherent stress testing output that can satisfy the needs of industry users and regulators. In part IV the author addresses more practical questions such as embedding the suggestions of the book into a viable governance structure.
Mathematics

Bayesian Networks

A Practical Guide to Applications

Author: Olivier Pourret,Patrick Naïm,Bruce Marcot

Publisher: John Wiley & Sons

ISBN: 9780470994542

Category: Mathematics

Page: 446

View: 4285

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.
Computers

Modeling and Reasoning with Bayesian Networks

Author: Adnan Darwiche

Publisher: Cambridge University Press

ISBN: 0521884381

Category: Computers

Page: 548

View: 1778

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.
Mathematics

Bayesian Decision Analysis

Principles and Practice

Author: Jim Q. Smith

Publisher: Cambridge University Press

ISBN: 1139491113

Category: Mathematics

Page: N.A

View: 2227

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.
Business & Economics

Bayesian Artificial Intelligence, Second Edition

Author: Kevin B. Korb,Ann E. Nicholson

Publisher: CRC Press

ISBN: 1439815925

Category: Business & Economics

Page: 491

View: 879

Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors discuss the use of Bayesian networks for causal modeling. They also draw on their own applied research to illustrate various applications of the technology. New to the Second Edition New chapter on Bayesian network classifiers New section on object-oriented Bayesian networks New section that addresses foundational problems with causal discovery and Markov blanket discovery New section that covers methods of evaluating causal discovery programs Discussions of many common modeling errors New applications and case studies More coverage on the uses of causal interventions to understand and reason with causal Bayesian networks Illustrated with real case studies, the second edition of this bestseller continues to cover the groundwork of Bayesian networks. It presents the elements of Bayesian network technology, automated causal discovery, and learning probabilities from data and shows how to employ these technologies to develop probabilistic expert systems. Web Resource The book’s website at www.csse.monash.edu.au/bai/book/book.html offers a variety of supplemental materials, including example Bayesian networks and data sets. Instructors can email the authors for sample solutions to many of the problems in the text.
Computers

Software Metrics

A Rigorous and Practical Approach, Third Edition

Author: Norman Fenton,James Bieman

Publisher: CRC Press

ISBN: 1439838232

Category: Computers

Page: 617

View: 6773

A Framework for Managing, Measuring, and Predicting Attributes of Software Development Products and Processes Reflecting the immense progress in the development and use of software metrics in the past decades, Software Metrics: A Rigorous and Practical Approach, Third Edition provides an up-to-date, accessible, and comprehensive introduction to software metrics. Like its popular predecessors, this third edition discusses important issues, explains essential concepts, and offers new approaches for tackling long-standing problems. New to the Third Edition This edition contains new material relevant to object-oriented design, design patterns, model-driven development, and agile development processes. It includes a new chapter on causal models and Bayesian networks and their application to software engineering. This edition also incorporates recent references to the latest software metrics activities, including research results, industrial case studies, and standards. Suitable for a Range of Readers With numerous examples and exercises, this book continues to serve a wide audience. It can be used as a textbook for a software metrics and quality assurance course or as a useful supplement in any software engineering course. Practitioners will appreciate the important results that have previously only appeared in research-oriented publications. Researchers will welcome the material on new results as well as the extensive bibliography of measurement-related information. The book also gives software managers and developers practical guidelines for selecting metrics and planning their use in a measurement program.
Business & Economics

Portfolio Management under Stress

A Bayesian-Net Approach to Coherent Asset Allocation

Author: Riccardo Rebonato,Alexander Denev

Publisher: Cambridge University Press

ISBN: 1107048117

Category: Business & Economics

Page: 516

View: 1462

A rigorous presentation of a novel methodology for asset allocation in financial portfolios under conditions of market distress.
Technology & Engineering

Guidelines for Fire Protection in Chemical, Petrochemical, and Hydrocarbon Processing Facilities

Author: CCPS (Center for Chemical Process Safety)

Publisher: Wiley-AIChE

ISBN: 9780816908981

Category: Technology & Engineering

Page: 480

View: 7838

While there are many resources available on fire protection andprevention in chemical petrochemical and petroleumplants—this is the first book that pulls them all together inone comprehensive resource. This book provides the tools todevelop, implement, and integrate a fire protection program into acompany or facility’s Risk Management System. This definitivevolume is a must-read for loss prevention managers, site managers,project managers, engineers and EHS professionals. Note: CD-ROM/DVD and other supplementary materials arenot included as part of eBook file.
Technology & Engineering

Basic Concepts of Industrial Hygiene

Author: RonaldM. Scott

Publisher: Routledge

ISBN: 135146468X

Category: Technology & Engineering

Page: 496

View: 3920

Basic Concepts of Industrial Hygiene covers the latest and most important topics in industrial hygiene today. The textbook begins with a look at the history and basis for industrial hygiene, which provides students with a foundation for understanding later developments. The book contains an in-depth discussion of new OSHA regulations, such as HAZWOPER and Process Safety, which deal with high hazard situations. It also features a chapter on biological hazards of current concern in health care, including tuberculosis, AIDS, and hepatitis B.
Business & Economics

Analyzing Risk through Probabilistic Modeling in Operations Research

Author: Jakóbczak, Dariusz Jacek

Publisher: IGI Global

ISBN: 1466694599

Category: Business & Economics

Page: 442

View: 9572

Probabilistic modeling represents a subject spanning many branches of mathematics, economics, and computer science to connect pure mathematics with applied sciences. Operational research also relies on this connection to enable the improvement of business functions and decision making. Analyzing Risk through Probabilistic Modeling in Operations Research is an authoritative reference publication discussing the various challenges in management and decision science. Featuring exhaustive coverage on a range of topics within operational research including, but not limited to, decision analysis, data mining, process modeling, probabilistic interpolation and extrapolation, and optimization methods, this book is an essential reference source for decision makers, academicians, researchers, advanced-level students, technology developers, and government officials interested in the implementation of probabilistic modeling in various business applications.
Technology & Engineering

Safety, Reliability and Risk Analysis

Beyond the Horizon

Author: R.D.J.M. Steenbergen,P.H.A.J.M. van Gelder,S. Miraglia,A.C.W.M. Vrouwenvelder

Publisher: CRC Press

ISBN: 1315815591

Category: Technology & Engineering

Page: 758

View: 3633

During the last decade there have been increasing societal concerns over sustainable developments focusing on the conservation of the environment, the welfare and safety of the individual and at the same time the optimal allocation of available natural and financial resources. As a consequence the methods of risk and reliability analysis are becoming increasingly important as decision support tools in various fields of engineering. In this book, the risk and reliability research community looks beyond the horizon. The technology we deploy to fix today’s problems is based on research that started more than two decades ago. What we are doing today should make a difference for tomorrow. Developing innovative new knowledge and applications helps engineers to better play the important role they have for society in establishing the basis for decision making. Safety, Reliability and Risk Analysis: Beyond the Horizon contains the papers presented at the 22nd European Safety and Reliability (ESREL 2013) annual conference in Amsterdam, The Netherlands. The abstracts book (785 pages) + full paper CD-ROM (3426 pages) cover a wide range of topics for which risk analysis forms an indispensable field of knowledge to ensure sufficient safety: Uncertainty Analysis, Accident and Incident Modeling, Human Factors and Human Reliability, System Reliability, Structural Reliability, Safety in Civil Engineering, Quantitative Risk Assessment, Prognostics and System Health Management, Occupational Safety, Mathematical Methods in Reliability and Safety, and Maintenance Modeling and Applications. Applications in different industrial areas are shown: Natural Hazards, Land Transportation, Aeronautics Aerospace, Chemical and Process Industry, Critical Infrastructures, Manufacturing, Security, Nuclear Industry, Energy, Maritime Transportation, and Information Technology.
Computers

Improving Homeland Security Decisions

Author: Ali E. Abbas,Milind Tambe,Detlof von Winterfeldt

Publisher: Cambridge University Press

ISBN: 1107161886

Category: Computers

Page: 600

View: 1753

What are the risks of terrorism and what are their consequences and economic impacts? Are we safer from terrorism today than before 9/11? Does the government spend our homeland security funds well? These questions motivated a twelve-year research program of the National Center for Risk and Economic Analysis of Terrorism Events (CREATE) at the University of Southern California, funded by the Department of Homeland Security. This book showcases some of the most important results of this research and offers key insights on how to address the most important security problems of our time. Written for homeland security researchers and practitioners, this book covers a wide range of methodologies and real-world examples of how to reduce terrorism risks, increase the efficient use of homeland security resources, and thereby make better decisions overall.
Mathematics

Computational Statistics

Author: Geof H. Givens,Jennifer A. Hoeting

Publisher: John Wiley & Sons

ISBN: 1118555481

Category: Mathematics

Page: 496

View: 2098

This new edition continues to serve as a comprehensive guide to modern and classical methods of statistical computing. The book is comprised of four main parts spanning the field: Optimization Integration and Simulation Bootstrapping Density Estimation and Smoothing Within these sections,each chapter includes a comprehensive introduction and step-by-step implementation summaries to accompany the explanations of key methods. The new edition includes updated coverage and existing topics as well as new topics such as adaptive MCMC and bootstrapping for correlated data. The book website now includes comprehensive R code for the entire book. There are extensive exercises, real examples, and helpful insights about how to use the methods in practice.