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Bayesian Networks

Author: Timo Koski
Publisher: John Wiley & Sons
ISBN: 1119964954
Size: 70.90 MB
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Bayesian Networks: An Introduction provides a self-contained introduction to the theory and applications of Bayesian networks, a topic of interest and importance for statisticians, computer scientists and those involved in modelling complex data sets. The material has been extensively tested in classroom teaching and assumes a basic knowledge of probability, statistics and mathematics. All notions are carefully explained and feature exercises throughout. Features include: An introduction to Dirichlet Distribution, Exponential Families and their applications. A detailed description of learning algorithms and Conditional Gaussian Distributions using Junction Tree methods. A discussion of Pearl's intervention calculus, with an introduction to the notion of see and do conditioning. All concepts are clearly defined and illustrated with examples and exercises. Solutions are provided online. This book will prove a valuable resource for postgraduate students of statistics, computer engineering, mathematics, data mining, artificial intelligence, and biology. Researchers and users of comparable modelling or statistical techniques such as neural networks will also find this book of interest.

Risk Assessment And Decision Analysis With Bayesian Networks Second Edition

Author: Norman Fenton
Publisher: CRC Press
ISBN: 1351978969
Size: 37.20 MB
Format: PDF, Kindle
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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.

Probabilistic Graphical Models

Author: Linda C. van der Gaag
Publisher: Springer
ISBN: 3319114336
Size: 33.53 MB
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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.

Bayesian Networks

Author: Olivier Pourret
Publisher: John Wiley & Sons
ISBN: 9780470994542
Size: 42.60 MB
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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.

Case Studies In Bayesian Statistical Modelling And Analysis

Author: Clair L. Alston
Publisher: John Wiley & Sons
ISBN: 1118394321
Size: 31.34 MB
Format: PDF, ePub, Mobi
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Provides an accessible foundation to Bayesian analysis usingreal world models This book aims to present an introduction to Bayesian modellingand computation, by considering real case studies drawn fromdiverse fields spanning ecology, health, genetics and finance. Eachchapter comprises a description of the problem, the correspondingmodel, the computational method, results and inferences as well asthe issues that arise in the implementation of theseapproaches. Case Studies in Bayesian Statistical Modelling andAnalysis: Illustrates how to do Bayesian analysis in a clear and concisemanner using real-world problems. Each chapter focuses on a real-world problem and describes theway in which the problem may be analysed using Bayesianmethods. Features approaches that can be used in a wide area ofapplication, such as, health, the environment, genetics,information science, medicine, biology, industry and remotesensing. Case Studies in Bayesian Statistical Modelling andAnalysis is aimed at statisticians, researchers andpractitioners who have some expertise in statistical modelling andanalysis, and some understanding of the basics of Bayesianstatistics, but little experience in its application. Graduatestudents of statistics and biostatistics will also find this bookbeneficial.

Innovations In Bayesian Networks

Author: Dawn E. Holmes
Publisher: Springer Science & Business Media
ISBN: 3540850651
Size: 20.97 MB
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Bayesian networks currently provide one of the most rapidly growing areas of research in computer science and statistics. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. Each of the twelve chapters is self-contained. Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Graduate students since it shows the direction of current research.

Bayesian Networks For Probabilistic Inference And Decision Analysis In Forensic Science

Author: Franco Taroni
Publisher: John Wiley & Sons
ISBN: 1118914740
Size: 16.57 MB
Format: PDF, Mobi
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"This book should have a place on the bookshelf of everyforensic scientist who cares about the science of evidenceinterpretation" Dr. Ian Evett, Principal Forensic Services Ltd, London, UK Continuing developments in science and technology mean that theamounts of information forensic scientists are able to provide forcriminal investigations is ever increasing. The commensurate increase in complexity creates difficulties forscientists and lawyers with regard to evaluation andinterpretation, notably with respect to issues of inference anddecision. Probability theory, implemented through graphical methods, andspecifically Bayesian networks, provides powerful methods to dealwith this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to thejudicial system. Bayesian Networks for Probabilistic Inference and DecisionAnalysis in Forensic Science provides a unique and comprehensiveintroduction to the use of Bayesian decision networks for theevaluation and interpretation of scientific findings in forensicscience, and for the support of decision-makers in their scientificand legal tasks. • Includes self-contained introductions to probabilityand decision theory. • Develops the characteristics of Bayesian networks,object-oriented Bayesian networks and their extension to decisionmodels. • Features implementation of the methodology withreference to commercial and academically available software. • Presents standard networks and their extensions thatcan be easily implemented and that can assist in the reader’sown analysis of real cases. • Provides a technique for structuring problems andorganizing data based on methods and principles of scientificreasoning. • Contains a method for the construction of coherent anddefensible arguments for the analysis and evaluation of scientificfindings and for decisions based on them. • Is written in a lucid style, suitable for forensicscientists and lawyers with minimal mathematical background. • Includes a foreword by Ian Evett. The clear and accessible style of this second edition makes thisbook ideal for all forensic scientists, applied statisticians andgraduate students wishing to evaluate forensic findings from theperspective of probability and decision analysis. It will alsoappeal to lawyers and other scientists and professionals interestedin the evaluation and interpretation of forensic findings,including decision making based on scientific information.

Introduction To Probability And Statistics For Ecosystem Managers

Author: Timothy C. Haas
Publisher: John Wiley & Sons
ISBN: 1118636236
Size: 35.26 MB
Format: PDF, Kindle
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Explores computer-intensive probability and statistics forecosystem management decision making Simulation is an accessible way to explain probability andstochastic model behavior to beginners. This book introducesprobability and statistics to future and practicing ecosystemmanagers by providing a comprehensive treatment of these two areas.The author presents a self-contained introduction for individualsinvolved in monitoring, assessing, and managing ecosystems andfeatures intuitive, simulation-based explanations of probabilisticand statistical concepts. Mathematical programming details areprovided for estimating ecosystem model parameters with MinimumDistance, a robust and computer-intensive method. The majority of examples illustrate how probability andstatistics can be applied to ecosystem management challenges. Thereare over 50 exercises – making this book suitable for alecture course in a natural resource and/or wildlife managementdepartment, or as the main text in a program of self-study. Key features: Reviews different approaches to wildlife and ecosystemmanagement and inference. Uses simulation as an accessible way to explain probability andstochastic model behavior to beginners. Covers material from basic probability through to hierarchicalBayesian models and spatial/ spatio-temporal statisticalinference. Provides detailed instructions for using R, along with completeR programs to recreate the output of the many examplespresented. Provides an introduction to Geographic Information Systems(GIS) along with examples from Quantum GIS, a free GIS softwarepackage. A companion website featuring all R code and data usedthroughout the book. Solutions to all exercises are presented along with an onlineintelligent tutoring system that supports readers who are using thebook for self-study.

Graphical Models

Author: Christian Borgelt
Publisher: John Wiley & Sons
ISBN: 9780470749562
Size: 15.40 MB
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Graphical models are of increasing importance in applied statistics, and in particular in data mining. Providing a self-contained introduction and overview to learning relational, probabilistic, and possibilistic networks from data, this second edition of Graphical Models is thoroughly updated to include the latest research in this burgeoning field, including a new chapter on visualization. The text provides graduate students, and researchers with all the necessary background material, including modelling under uncertainty, decomposition of distributions, graphical representation of distributions, and applications relating to graphical models and problems for further research.