Paul Bayes's Books

Liberty's Apostle - Richard Price, His Life and Times

By: Paul Frame

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Liberty's Apostle - Richard Price, His Life and Times

By: Paul Frame

It introduces readers to a man largely unknown outside academia but who was considered by his contemporaries to be one of the greatest thinkers of the Enlightenment and who championed, against powerful opposition, many of the rights and liberty’s we take for granted today. As a chronological account it covers and discusses Price’s writing on all the issues which interested him. Among them are political and civil liberty, parliamentary reform, life assurance, mathematics, moral philosophy and the American and French Revolutions. His comments on all these are as important today, and as enlightening, as they were in his time. The book is the first to make extensive use of Price’s correspondence with the likes of Joseph Priestley, Benjamin Franklin, Thomas Jefferson and newly discovered letters from Price’s nephew in Paris during the July 1789 Revolution. This coupled with the chronological approach gives the reader an insight into his thinking and political developments during crucial periods of the eighteenth century Enlightenment and provides a high readable narrative for the general reader.

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339

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University of Wales Press

Published Date

ISBN 10

1783162171

ISBN 13

9781783162178

Python for Data Science For Dummies

By: John Paul Mueller,Luca Massaron

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Python for Data Science For Dummies

By: John Paul Mueller,Luca Massaron

The fast and easy way to learn Python programming and statistics Python is a general-purpose programming language created in the late 1980s—and named after Monty Python—that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library. Python For Data Science For Dummies is written for people who are new to data analysis, and discusses the basics of Python data analysis programming and statistics. The book also discusses Google Colab, which makes it possible to write Python code in the cloud. Get started with data science and Python Visualize information Wrangle data Learn from data The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction.

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496

Publisher

John Wiley & Sons

Published Date

ISBN 10

1119547644

ISBN 13

9781119547648

Data Science Programming All-in-One For Dummies

By: John Paul Mueller,Luca Massaron

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Data Science Programming All-in-One For Dummies

By: John Paul Mueller,Luca Massaron

Your logical, linear guide to the fundamentals of data science programming Data science is exploding—in a good way—with a forecast of 1.7 megabytes of new information created every second for each human being on the planet by 2020 and 11.5 million job openings by 2026. It clearly pays dividends to be in the know. This friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear regression, logical regression, machine learning, neural networks, recommender engines, and cross-validation of models. Data Science Programming All-In-One For Dummies is a compilation of the key data science, machine learning, and deep learning programming languages: Python and R. It helps you decide which programming languages are best for specific data science needs. It also gives you the guidelines to build your own projects to solve problems in real time. Get grounded: the ideal start for new data professionals What lies ahead: learn about specific areas that data is transforming Be meaningful: find out how to tell your data story See clearly: pick up the art of visualization Whether you’re a beginning student or already mid-career, get your copy now and add even more meaning to your life—and everyone else’s!

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768

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John Wiley & Sons

Published Date

ISBN 10

1119626110

ISBN 13

9781119626114

Artificial Intelligence For Dummies

By: John Paul Mueller,Luca Massaron

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Artificial Intelligence For Dummies

By: John Paul Mueller,Luca Massaron

Forget far-away dreams of the future. Artificial intelligence is here now! Every time you use a smart device or some sort of slick technology—be it a smartwatch, smart speaker, security alarm, or even customer service chat box—you’re engaging with artificial intelligence (AI). If you’re curious about how AI is developed—or question whether AI is real—Artificial Intelligence For Dummies holds the answers you’re looking for. Starting with a basic definition of AI and explanations of data use, algorithms, special hardware, and more, this reference simplifies this complex topic for anyone who wants to understand what operates the devices we can’t live without. This book will help you: Separate the reality of artificial intelligence from the hype Know what artificial intelligence can accomplish and what its limits are Understand how AI speeds up data gathering and analysis to help you make informed decisions more quickly See how AI is being used in hardware applications like drones, robots, and vehicles Know where AI could be used in space, medicine, and communication fields sooner than you think Almost 80 percent of the devices you interact with every day depend on some sort of AI. And although you don’t need to understand AI to operate your smart speaker or interact with a bot, you’ll feel a little smarter—dare we say more intelligent—when you know what’s going on behind the scenes. So don’t wait. Pick up this popular guide to unlock the secrets of AI today!

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368

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John Wiley & Sons

Published Date

ISBN 10

1119796784

ISBN 13

9781119796787

Doing Better Statistics in Human-Computer Interaction

By: Paul Cairns

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Doing Better Statistics in Human-Computer Interaction

By: Paul Cairns

This book addresses common questions from HCI researchers when trying to do statistical analysis on their data.

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253

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Cambridge University Press

Published Date

ISBN 10

110848252X

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9781108482523

Building Statistical Models in Python

Develop useful models for regression, classification, time series, and survival analysis

By: Huy Hoang Nguyen,Paul N Adams,Stuart J Miller

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Building Statistical Models in Python

Develop useful models for regression, classification, time series, and survival analysis

By: Huy Hoang Nguyen,Paul N Adams,Stuart J Miller

Make data-driven, informed decisions and enhance your statistical expertise in Python by turning raw data into meaningful insights Purchase of the print or Kindle book includes a free PDF eBook Key Features Gain expertise in identifying and modeling patterns that generate success Explore the concepts with Python using important libraries such as stats models Learn how to build models on real-world data sets and find solutions to practical challenges Book DescriptionThe ability to proficiently perform statistical modeling is a fundamental skill for data scientists and essential for businesses reliant on data insights. Building Statistical Models with Python is a comprehensive guide that will empower you to leverage mathematical and statistical principles in data assessment, understanding, and inference generation. This book not only equips you with skills to navigate the complexities of statistical modeling, but also provides practical guidance for immediate implementation through illustrative examples. Through emphasis on application and code examples, you’ll understand the concepts while gaining hands-on experience. With the help of Python and its essential libraries, you’ll explore key statistical models, including hypothesis testing, regression, time series analysis, classification, and more. By the end of this book, you’ll gain fluency in statistical modeling while harnessing the full potential of Python's rich ecosystem for data analysis.What you will learn Explore the use of statistics to make decisions under uncertainty Answer questions about data using hypothesis tests Understand the difference between regression and classification models Build models with stats models in Python Analyze time series data and provide forecasts Discover Survival Analysis and the problems it can solve Who this book is forIf you are looking to get started with building statistical models for your data sets, this book is for you! Building Statistical Models in Python bridges the gap between statistical theory and practical application of Python. Since you’ll take a comprehensive journey through theory and application, no previous knowledge of statistics is required, but some experience with Python will be useful.

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420

Publisher

Packt Publishing Ltd

Published Date

ISBN 10

1804612154

ISBN 13

9781804612156

Roberts & Zuckerman's Criminal Evidence

By: Paul Roberts

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Roberts & Zuckerman's Criminal Evidence

By: Paul Roberts

Roberts and Zuckerman's Criminal Evidence is the eagerly-anticipated third of edition of the market-leading text on criminal evidence, fully revised to take account of developments in legislation, case-law, policy debates, and academic commentary during the decade since the previous edition was published. With an explicit focus on the rules and principles of criminal trial procedure, Roberts and Zuckerman's Criminal Evidence develops a coherent account of evidence law which is doctrinally detailed, securely grounded in a normative theoretical framework, and sensitive to the institutional and socio-legal factors shaping criminal litigation in practice. The book is designed to be accessible to the beginner, informative to the criminal court judge or legal practitioner, and thought-provoking to the advanced student and scholar: a textbook and monograph rolled into one. The book also provides an ideal disciplinary map and work of reference to introduce non-lawyers (including forensic scientists and other expert witnesses) to the foundational assumptions and technical intricacies of criminal trial procedure in England and Wales, and will be an invaluable resource for courts, lawyers and scholars in other jurisdictions seeking comparative insight and understanding of evidentiary regulation in the common law tradition.

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1193

Publisher

Oxford University Press

Published Date

ISBN 10

0192557912

ISBN 13

9780192557919

Computational Learning Theory

4th European Conference, EuroCOLT'99 Nordkirchen, Germany, March 29-31, 1999 Proceedings

By: Paul Fischer

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Computational Learning Theory

4th European Conference, EuroCOLT'99 Nordkirchen, Germany, March 29-31, 1999 Proceedings

By: Paul Fischer

This book constitutes the refereed proceedings of the 4th European Conference on Computational Learning Theory, EuroCOLT'99, held in Nordkirchen, Germany in March 1999. The 21 revised full papers presented were selected from a total of 35 submissions; also included are two invited contributions. The book is divided in topical sections on learning from queries and counterexamples, reinforcement learning, online learning and export advice, teaching and learning, inductive inference, and statistical theory of learning and pattern recognition.

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311

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Springer Science & Business Media

Published Date

ISBN 10

3540657010

ISBN 13

9783540657019

An Introduction to Kolmogorov Complexity and Its Applications

By: Ming Li,Paul Vitányi

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An Introduction to Kolmogorov Complexity and Its Applications

By: Ming Li,Paul Vitányi

This must-read textbook presents an essential introduction to Kolmogorov complexity (KC), a central theory and powerful tool in information science that deals with the quantity of information in individual objects. The text covers both the fundamental concepts and the most important practical applications, supported by a wealth of didactic features. This thoroughly revised and enhanced fourth edition includes new and updated material on, amongst other topics, the Miller-Yu theorem, the Gács-Kučera theorem, the Day-Gács theorem, increasing randomness, short lists computable from an input string containing the incomputable Kolmogorov complexity of the input, the Lovász local lemma, sorting, the algorithmic full Slepian-Wolf theorem for individual strings, multiset normalized information distance and normalized web distance, and conditional universal distribution. Topics and features: describes the mathematical theory of KC, including the theories of algorithmic complexity and algorithmic probability; presents a general theory of inductive reasoning and its applications, and reviews the utility of the incompressibility method; covers the practical application of KC in great detail, including the normalized information distance (the similarity metric) and information diameter of multisets in phylogeny, language trees, music, heterogeneous files, and clustering; discusses the many applications of resource-bounded KC, and examines different physical theories from a KC point of view; includes numerous examples that elaborate the theory, and a range of exercises of varying difficulty (with solutions); offers explanatory asides on technical issues, and extensive historical sections; suggests structures for several one-semester courses in the preface. As the definitive textbook on Kolmogorov complexity, this comprehensive and self-contained work is an invaluable resource for advanced undergraduate students, graduate students, and researchers in all fields of science.

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852

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Springer

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ISBN 10

3030112985

ISBN 13

9783030112981

Statistical Inference

By: Paul H. Garthwaite,I. T. Jolliffe,Byron Jones

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Statistical Inference

By: Paul H. Garthwaite,I. T. Jolliffe,Byron Jones

Statistical inference is the foundation on which much of statistical practice is built. The book covers the topic at a level suitable for students and professionals who need to understand these foundations.

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346

Publisher

OUP Oxford

Published Date

ISBN 10

0198572263

ISBN 13

9780198572268

Data Mining for the Social Sciences

An Introduction

By: Paul Attewell,David Monaghan

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Data Mining for the Social Sciences

An Introduction

By: Paul Attewell,David Monaghan

The amount of information collected on human behavior every day is staggering, and exponentially greater than at any time in the past. At the same time, we are inundated by stories of powerful algorithms capable of churning through this sea of data and uncovering patterns. These techniques go by many names - data mining, predictive analytics, machine learning - and they are being used by governments as they spy on citizens and by huge corporations are they fine-tune their advertising strategies. And yet social scientists continue mainly to employ a set of analytical tools developed in an earlier era when data was sparse and difficult to come by. In this timely book, Paul Attewell and David Monaghan provide a simple and accessible introduction to Data Mining geared towards social scientists. They discuss how the data mining approach differs substantially, and in some ways radically, from that of conventional statistical modeling familiar to most social scientists. They demystify data mining, describing the diverse set of techniques that the term covers and discussing the strengths and weaknesses of the various approaches. Finally they give practical demonstrations of how to carry out analyses using data mining tools in a number of statistical software packages. It is the hope of the authors that this book will empower social scientists to consider incorporating data mining methodologies in their analytical toolkits"--Provided by publisher.

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264

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Univ of California Press

Published Date

ISBN 10

0520280989

ISBN 13

9780520280984

Sociable Criticism in England, 1625-1725

By: Paul Trolander,Zeynep Tenger

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Sociable Criticism in England, 1625-1725

By: Paul Trolander,Zeynep Tenger

Sociable Criticism in England explores how from 1625 to 1725 cultural practices and discourses of sociability (rules for small-group discussion, friendship discourse, and patron-client relationships) determined the venues within which critical judgments were rendered, disseminated, and received. It establishes how individuals operating in small groups were authorized to circulate critical judgments and commentary, why certain modes of critical exchange were treated as beyond the ken of good social manners, and how such expectations were subverted or manipulated to avoid the imputation that individuals had violated the standards for offering public criticism. Philips, George Villiers, John Dryden, Lady Margaret Cavendish, John Dennis, and Joseph Addison, this study argues that seventeenth- and early eighteenth-century criticism could circulate either orally, in manuscript, or in print so long as it appeared to originate in interpersonal encounters considered appropriate to critical discussion.

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242

Publisher

University of Delaware Press

Published Date

ISBN 10

0874139694

ISBN 13

9780874139693

Practical Data Science with SAP

Machine Learning Techniques for Enterprise Data

By: Greg Foss,Paul Modderman

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Practical Data Science with SAP

Machine Learning Techniques for Enterprise Data

By: Greg Foss,Paul Modderman

Learn how to fuse today's data science tools and techniques with your SAP enterprise resource planning (ERP) system. With this practical guide, SAP veterans Greg Foss and Paul Modderman demonstrate how to use several data analysis tools to solve interesting problems with your SAP data. Data engineers and scientists will explore ways to add SAP data to their analysis processes, while SAP business analysts will learn practical methods for answering questions about the business. By focusing on grounded explanations of both SAP processes and data science tools, this book gives data scientists and business analysts powerful methods for discovering deep data truths. You'll explore: Examples of how data analysis can help you solve several SAP challenges Natural language processing for unlocking the secrets in text Data science techniques for data clustering and segmentation Methods for detecting anomalies in your SAP data Data visualization techniques for making your data come to life

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329

Publisher

"O'Reilly Media, Inc."

Published Date

ISBN 10

1492046450

ISBN 13

9781492046455

Expert Evidence and Scientific Proof in Criminal Trials

By: Paul Roberts

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Expert Evidence and Scientific Proof in Criminal Trials

By: Paul Roberts

Forensic science evidence and expert witness testimony play an increasingly prominent role in modern criminal proceedings. Science produces powerful evidence of criminal offending, but has also courted controversy and sometimes contributed towards miscarriages of justice. The twenty-six articles and essays reproduced in this volume explore the theoretical foundations of modern scientific proof and critically consider the practical issues to which expert evidence gives rise in contemporary criminal trials. The essays are prefaced by a substantial new introduction which provides an overview and incisive commentary contextualising the key debates. The volume begins by placingforensic science in interdisciplinary focus, with contributions from historical, sociological, Science and Technology Studies (STS), philosophical and jurisprudential perspectives. This is followed by closer examination of the role of forensic science and other expert evidence in criminal proceedings, exposing enduring tensions and addressing recent controversies in the relationship between science and criminal law. A third set of contributions considers the practical challenges of interpreting and communicating forensic science evidence. This perennial battle continues to be fought at the intersection between the logic of scientific inference and the psychology of the fact-finder‘scommon sense reasoning. Finally, the volume‘s fourth group of essays evaluates the (limited) success of existing procedural reforms aimed at improving the reception of expert testimony in criminal adjudication, and considers future prospects for institutional renewal - with a keen eye to comparative law models and experiences, success stories and cautionary tales.

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398

Publisher

Routledge

Published Date

ISBN 10

135156739X

ISBN 13

9781351567398

Criminal Evidence

By: Paul Roberts,Adrian Zuckerman

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Criminal Evidence

By: Paul Roberts,Adrian Zuckerman

Based on Adrian Zuckerman's 'The Principles of Criminal Evidence', this book presents a comprehensive treatment of the fundamental principles & underlying logic of the law of criminal evidence. It includes changes relating to presumption of innocence, privilege against self-incrimination, character, & the law of corroboration.

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772

Publisher

Oxford University Press

Published Date

ISBN 10

0199231648

ISBN 13

9780199231645

Statistical Applications for the Behavioral and Social Sciences

By: K. Paul Nesselroade, Jr.,Laurence G. Grimm

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Statistical Applications for the Behavioral and Social Sciences

By: K. Paul Nesselroade, Jr.,Laurence G. Grimm

An updated edition of a classic text on applying statistical analyses to the social sciences, with reviews, new chapters, an expanded set of post-hoc analyses, and information on computing in Excel and SPSS Now in its second edition,Statistical Applications for the Behavioral and Social Sciences has been revised and updated and continues to offer an essential guide to the conceptual foundations of statistical analyses (particularly inferential statistics), placing an emphasis on connecting statistical tools with appropriate research contexts. Designed to be accessible, the text contains an applications-oriented, step-by-step presentation of the statistical theories and formulas most often used by the social sciences. The revised text also includes an entire chapter on the basic concepts in research, presenting an overall context for all the book's statistical theories and formulas. The authors cover descriptive statistics and z scores, the theoretical underpinnings of inferential statistics, z and t tests, power analysis, one/two-way and repeated-measures ANOVA, linear correlation and regression, as well as chi-square and other nonparametric tests. The second edition also includes a new chapter on basic probability theory. This important resource: Contains information regarding the use of statistical software packages; both Excel and SPSS Offers four strategically positioned and accumulating reviews, each containing a set of research-oriented diagnostic questions designed to help students determine which tests are applicable to which research scenarios Incorporates additional statistical information on follow-up analyses such as post-hoc tests and effect sizes Includes a series of sidebar discussions dispersed throughout the text that address, among other topics, the recent and growing controversy regarding the failed reproducibility of published findings in the social sciences Puts renewed emphasis on presentation of data and findings using the APA format Includes supplementary material consisting of a set of "kick-start" quizzes designed to get students quickly back up to speed at the start of an instructional period, and a complete set of ready-to-use PowerPoint slides for in-class use Written for students in areas such as psychology, sociology, criminology, political science, public health, and others, Statistical Applications for the Behavioral and Social Sciences, Second Edition continues to provide the information needed to understand the foundations of statistical analyses as relevant to the behavioral and social sciences.

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964

Publisher

John Wiley & Sons

Published Date

ISBN 10

1119355397

ISBN 13

9781119355397

R for Medicine and Biology

By: Paul D. Lewis

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R for Medicine and Biology

By: Paul D. Lewis

R is quickly becoming the number one choice for users in the fields of biology, medicine, and bioinformatics as their main means of storing, processing, sharing, and analyzing biomedical data. R for Medicine and Biology is a step-by-step guide through the use of the statistical environment R, as used in a biomedical domain. Ideal for healthcare professionals, scientists, informaticists, and statistical experts, this resource will provide even the novice programmer with the tools necessary to process and analyze their data using the R environment. Introductory chapters guide readers in how to obtain, install, and become familiar with R and provide a clear introduction to the programming language using numerous worked examples. Later chapters outline how R can be used, not just for biomedical data analysis, but also as an environment for the processing, storing, reporting, and sharing of data and results. The remainder of the book explores areas of R application to common domains of biomedical informatics, including imaging, statistical analysis, data mining/modeling, pathology informatics, epidemiology, clinical trials, and metadata usage. R for Medicine and Biology will provide you with a single desk reference for the R environment and its many capabilities.

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422

Publisher

Jones & Bartlett Learning

Published Date

ISBN 10

1449633145

ISBN 13

9781449633141

Decision Making in Health and Medicine

Integrating Evidence and Values

By: M. G. Myriam Hunink,Milton C. Weinstein,Eve Wittenberg,Michael F. Drummond,Joseph S. Pliskin,John B. Wong,Paul P. Glasziou

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Decision Making in Health and Medicine

Integrating Evidence and Values

By: M. G. Myriam Hunink,Milton C. Weinstein,Eve Wittenberg,Michael F. Drummond,Joseph S. Pliskin,John B. Wong,Paul P. Glasziou

Decision making in health care involves consideration of a complex set of diagnostic, therapeutic and prognostic uncertainties. Medical therapies have side effects, surgical interventions may lead to complications, and diagnostic tests can produce misleading results. Furthermore, patient values and service costs must be considered. Decisions in clinical and health policy require careful weighing of risks and benefits and are commonly a trade-off of competing objectives: maximizing quality of life vs maximizing life expectancy vs minimizing the resources required. This text takes a proactive, systematic and rational approach to medical decision making. It covers decision trees, Bayesian revision, receiver operating characteristic curves, and cost-effectiveness analysis, as well as advanced topics such as Markov models, microsimulation, probabilistic sensitivity analysis and value of information analysis. It provides an essential resource for trainees and researchers involved in medical decision modelling, evidence-based medicine, clinical epidemiology, comparative effectiveness, public health, health economics, and health technology assessment.

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447

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Cambridge University Press

Published Date

ISBN 10

1316062317

ISBN 13

9781316062319

Nonparametric Statistics with Applications to Science and Engineering

By: Paul H. Kvam,Brani Vidakovic

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Nonparametric Statistics with Applications to Science and Engineering

By: Paul H. Kvam,Brani Vidakovic

A thorough and definitive book that fully addresses traditional and modern-day topics of nonparametric statistics This book presents a practical approach to nonparametric statistical analysis and provides comprehensive coverage of both established and newly developed methods. With the use of MATLAB, the authors present information on theorems and rank tests in an applied fashion, with an emphasis on modern methods in regression and curve fitting, bootstrap confidence intervals, splines, wavelets, empirical likelihood, and goodness-of-fit testing. Nonparametric Statistics with Applications to Science and Engineering begins with succinct coverage of basic results for order statistics, methods of categorical data analysis, nonparametric regression, and curve fitting methods. The authors then focus on nonparametric procedures that are becoming more relevant to engineering researchers and practitioners. The important fundamental materials needed to effectively learn and apply the discussed methods are also provided throughout the book. Complete with exercise sets, chapter reviews, and a related Web site that features downloadable MATLAB applications, this book is an essential textbook for graduate courses in engineering and the physical sciences and also serves as a valuable reference for researchers who seek a more comprehensive understanding of modern nonparametric statistical methods.

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448

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John Wiley & Sons

Published Date

ISBN 10

0470168692

ISBN 13

9780470168691

Explanatory Item Response Models

A Generalized Linear and Nonlinear Approach

By: Paul de Boeck

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Explanatory Item Response Models

A Generalized Linear and Nonlinear Approach

By: Paul de Boeck

This edited volume gives a new and integrated introduction to item response models (predominantly used in measurement applications in psychology, education, and other social science areas) from the viewpoint of the statistical theory of generalized linear and nonlinear mixed models. The new framework allows the domain of item response models to be co-ordinated and broadened to emphasize their explanatory uses beyond their standard descriptive uses. The basic explanatory principle is that item responses can be modeled as a function of predictors of various kinds. The predictors can be (a) characteristics of items, of persons, and of combinations of persons and items; (b) observed or latent (of either items or persons); and they can be (c) latent continuous or latent categorical. In this way a broad range of models is generated, including a wide range of extant item response models as well as some new ones. Within this range, models with explanatory predictors are given special attention in this book, but we also discuss descriptive models. Note that the term "item responses" does not just refer to the traditional "test data," but are broadly conceived as categorical data from a repeated observations design. Hence, data from studies with repeated observations experimental designs, or with longitudinal designs, may also be modelled. The book starts with a four-chapter section containing an introduction to the framework. The remaining chapters describe models for ordered-category data, multilevel models, models for differential item functioning, multidimensional models, models for local item dependency, and mixture models. It also includes a chapter on the statistical background and one on useful software. In order to make the task easier for the reader, a unified approach to notation and model description is followed throughout the chapters, and a single data set is used in most examples to make it easier to see how the many models are related. For all major examples, computer commands from the SAS package are provided that can be used to estimate the results for each model. In addition, sample commands are provided for other major computer packages. Paul De Boeck is Professor of Psychology at K.U. Leuven (Belgium), and Mark Wilson is Professor of Education at UC Berkeley (USA). They are also co-editors (along with Pamela Moss) of a new journal entitled Measurement: Interdisciplinary Research and Perspectives. The chapter authors are members of a collaborative group of psychometricians and statisticians centered on K.U. Leuven and UC Berkeley.

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416

Publisher

Springer Science & Business Media

Published Date

ISBN 10

0387402756

ISBN 13

9780387402758

Nonparametric Statistics with Applications to Science and Engineering with R

By: Paul Kvam,Brani Vidakovic,Seong-joon Kim

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Nonparametric Statistics with Applications to Science and Engineering with R

By: Paul Kvam,Brani Vidakovic,Seong-joon Kim

NONPARAMETRIC STATISTICS WITH APPLICATIONS TO SCIENCE AND ENGINEERING WITH R Introduction to the methods and techniques of traditional and modern nonparametric statistics, incorporating R code Nonparametric Statistics with Applications to Science and Engineering with R presents modern nonparametric statistics from a practical point of view, with the newly revised edition including custom R functions implementing nonparametric methods to explain how to compute them and make them more comprehensible. Relevant built-in functions and packages on CRAN are also provided with a sample code. R codes in the new edition not only enable readers to perform nonparametric analysis easily, but also to visualize and explore data using R’s powerful graphic systems, such as ggplot2 package and R base graphic system. The new edition includes useful tables at the end of each chapter that help the reader find data sets, files, functions, and packages that are used and relevant to the respective chapter. New examples and exercises that enable readers to gain a deeper insight into nonparametric statistics and increase their comprehension are also included. Some of the sample topics discussed in Nonparametric Statistics with Applications to Science and Engineering with R include: Basics of probability, statistics, Bayesian statistics, order statistics, Kolmogorov–Smirnov test statistics, rank tests, and designed experiments Categorical data, estimating distribution functions, density estimation, least squares regression, curve fitting techniques, wavelets, and bootstrap sampling EM algorithms, statistical learning, nonparametric Bayes, WinBUGS, properties of ranks, and Spearman coefficient of rank correlation Chi-square and goodness-of-fit, contingency tables, Fisher exact test, MC Nemar test, Cochran’s test, Mantel–Haenszel test, and Empirical Likelihood Nonparametric Statistics with Applications to Science and Engineering with R is a highly valuable resource for graduate students in engineering and the physical and mathematical sciences, as well as researchers who need a more comprehensive, but succinct understanding of modern nonparametric statistical methods.

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452

Publisher

John Wiley & Sons

Published Date

ISBN 10

1119268133

ISBN 13

9781119268130

Statistical Methods for Healthcare Performance Monitoring

By: Alex Bottle,Paul Aylin

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Statistical Methods for Healthcare Performance Monitoring

By: Alex Bottle,Paul Aylin

Healthcare is important to everyone, yet large variations in its quality have been well documented both between and within many countries. With demand and expenditure rising, it’s more crucial than ever to know how well the healthcare system and all its components – from staff member to regional network – are performing. This requires data, which inevitably differ in form and quality. It also requires statistical methods, the output of which needs to be presented so that it can be understood by whoever needs it to make decisions. Statistical Methods for Healthcare Performance Monitoring covers measuring quality, types of data, risk adjustment, defining good and bad performance, statistical monitoring, presenting the results to different audiences and evaluating the monitoring system itself. Using examples from around the world, it brings all the issues and perspectives together in a largely non-technical way for clinicians, managers and methodologists. Statistical Methods for Healthcare Performance Monitoring is aimed at statisticians and researchers who need to know how to measure and compare performance, health service regulators, health service managers with responsibilities for monitoring performance, and quality improvement scientists, including those involved in clinical audits.

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292

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CRC Press

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1482246104

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9781482246100

Machine Learning For Dummies

By: John Paul Mueller,Luca Massaron

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Machine Learning For Dummies

By: John Paul Mueller,Luca Massaron

Your comprehensive entry-level guide to machine learning While machine learning expertise doesn’t quite mean you can create your own Turing Test-proof android—as in the movie Ex Machina—it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and solving problems fast and on a large scale. Anyone who masters the principles of machine learning is mastering a big part of our tech future and opening up incredible new directions in careers that include fraud detection, optimizing search results, serving real-time ads, credit-scoring, building accurate and sophisticated pricing models—and way, way more. Unlike most machine learning books, the fully updated 2nd Edition of Machine Learning For Dummies doesn't assume you have years of experience using programming languages such as Python (R source is also included in a downloadable form with comments and explanations), but lets you in on the ground floor, covering the entry-level materials that will get you up and running building models you need to perform practical tasks. It takes a look at the underlying—and fascinating—math principles that power machine learning but also shows that you don't need to be a math whiz to build fun new tools and apply them to your work and study. Understand the history of AI and machine learning Work with Python 3.8 and TensorFlow 2.x (and R as a download) Build and test your own models Use the latest datasets, rather than the worn out data found in other books Apply machine learning to real problems Whether you want to learn for college or to enhance your business or career performance, this friendly beginner's guide is your best introduction to machine learning, allowing you to become quickly confident using this amazing and fast-developing technology that's impacting lives for the better all over the world.

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464

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John Wiley & Sons

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1119724066

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9781119724063

Statistics in a Nutshell

A Desktop Quick Reference

By: Sarah Boslaugh,Dr. Paul Andrew Watters

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Statistics in a Nutshell

A Desktop Quick Reference

By: Sarah Boslaugh,Dr. Paul Andrew Watters

Need to learn statistics as part of your job, or want some help passing a statistics course? Statistics in a Nutshell is a clear and concise introduction and reference that's perfect for anyone with no previous background in the subject. This book gives you a solid understanding of statistics without being too simple, yet without the numbing complexity of most college texts. You get a firm grasp of the fundamentals and a hands-on understanding of how to apply them before moving on to the more advanced material that follows. Each chapter presents you with easy-to-follow descriptions illustrated by graphics, formulas, and plenty of solved examples. Before you know it, you'll learn to apply statistical reasoning and statistical techniques, from basic concepts of probability and hypothesis testing to multivariate analysis. Organized into four distinct sections, Statistics in a Nutshell offers you: Introductory material: Different ways to think about statistics Basic concepts of measurement and probability theory Data management for statistical analysis Research design and experimental design How to critique statistics presented by others Basic inferential statistics: Basic concepts of inferential statistics The concept of correlation, when it is and is not an appropriate measure of association Dichotomous and categorical data The distinction between parametric and nonparametric statistics Advanced inferential techniques: The General Linear Model Analysis of Variance (ANOVA) and MANOVA Multiple linear regression Specialized techniques: Business and quality improvement statistics Medical and public health statistics Educational and psychological statistics Unlike many introductory books on the subject, Statistics in a Nutshell doesn't omit important material in an effort to dumb it down. And this book is far more practical than most college texts, which tend to over-emphasize calculation without teaching you when and how to apply different statistical tests. With Statistics in a Nutshell, you learn how to perform most common statistical analyses, and understand statistical techniques presented in research articles. If you need to know how to use a wide range of statistical techniques without getting in over your head, this is the book you want.

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480

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"O'Reilly Media, Inc."

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1449397816

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9781449397814

Machine Learning in Finance

From Theory to Practice

By: Matthew F. Dixon,Igor Halperin,Paul Bilokon

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Machine Learning in Finance

From Theory to Practice

By: Matthew F. Dixon,Igor Halperin,Paul Bilokon

This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.

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565

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Springer Nature

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3030410684

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9783030410681

Philosophy of Medicine

An Introduction

By: R. Paul Thompson,Ross E.G. Upshur

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Philosophy of Medicine

An Introduction

By: R. Paul Thompson,Ross E.G. Upshur

What kind of knowledge is medical knowledge? Can medicine be explained scientifically? Is disease a scientific concept, or do explanations of disease depend on values? What is "evidence-based" medicine? Are advances in neuroscience bringing us closer to a scientific understanding of the mind? The nature of medicine raises fundamental questions about explanation, causation, knowledge and ontology – questions that are central to philosophy as well as medicine. This book introduces the fundamental issues in philosophy of medicine for those coming to the subject for the first time, including: • understanding the physician–patient relationship: the phenomenology of the medical encounter. • Models and theories in biology and medicine: what role do theories play in medicine? Are they similar to scientific theories? • Randomised controlled trials: can scientific experiments be replicated in clinical medicine? What are the philosophical criticisms levelled at RCTs? • The concept of evidence in medical research: what do we mean by "evidence-based medicine"? Should all medicine be based on evidence? • Causation in medicine. • What do advances in neuroscience reveal about the relationship between mind and body? • Defining health and disease: are explanations of disease objective or do they depend on values? • Evolutionary medicine: what is the role of evolutionary biology in understanding medicine? Is it relevant? Extensive use of empirical examples and case studies are included throughout, including debates about smoking and cancer, the use of placebos in randomised controlled trials, controversies about PSA testing and research into the causes of HIV. This is an indispensable introduction to those teaching philosophy of medicine and philosophy of science.

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208

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Routledge

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1351660748

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9781351660747

Discrete Multivariate Analysis

Theory and Practice

By: Yvonne M. Bishop,Stephen E. Fienberg,Paul W. Holland

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Discrete Multivariate Analysis

Theory and Practice

By: Yvonne M. Bishop,Stephen E. Fienberg,Paul W. Holland

“A welcome addition to multivariate analysis. The discussion is lucid and very leisurely, excellently illustrated with applications drawn from a wide variety of fields. A good part of the book can be understood without very specialized statistical knowledge. It is a most welcome contribution to an interesting and lively subject.” -- Nature Originally published in 1974, this book is a reprint of a classic, still-valuable text.

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558

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Springer Science & Business Media

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0387728058

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9780387728056

Something Doesn’t Add Up

Surviving Statistics in a Number-Mad World

By: Paul Goodwin

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Something Doesn’t Add Up

Surviving Statistics in a Number-Mad World

By: Paul Goodwin

Some people fear and mistrust numbers. Others want to use them for everything. After a long career as a statistician, Paul Goodwin has learned the hard way that the ones who want to use them for everything are a very good reason for the rest of us to fear and mistrust them. Something Doesn't Add Up is a fieldguide to the numbers that rule our world, even though they don't make sense. Wry, witty and humane, Goodwin explains mathematical subtleties so painlessly that you hardly need to think about numbers at all. He demonstrates how statistics that are meant to make life simpler often make it simpler than it actually is, but also reveals some of the ways we really can use maths to make better decisions. Enter the world of fitness tracking, the history of IQ testing, China's social credit system, Effective Altruism, and learn how someone should have noticed that Harold Shipman was killing his patients years before they actually did. In the right hands, maths is a useful tool. It's just a pity there are so many of the wrong hands about.

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226

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Profile Books

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1782835490

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9781782835493

Measurement Error and Misclassification in Statistics and Epidemiology

Impacts and Bayesian Adjustments

By: Paul Gustafson

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Measurement Error and Misclassification in Statistics and Epidemiology

Impacts and Bayesian Adjustments

By: Paul Gustafson

Mismeasurement of explanatory variables is a common hazard when using statistical modeling techniques, and particularly so in fields such as biostatistics and epidemiology where perceived risk factors cannot always be measured accurately. With this perspective and a focus on both continuous and categorical variables, Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments examines the consequences and Bayesian remedies in those cases where the explanatory variable cannot be measured with precision. The author explores both measurement error in continuous variables and misclassification in discrete variables, and shows how Bayesian methods might be used to allow for mismeasurement. A broad range of topics, from basic research to more complex concepts such as "wrong-model" fitting, make this a useful research work for practitioners, students and researchers in biostatistics and epidemiology.

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213

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CRC Press

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1135441235

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9781135441234

Contemporary Sport Management

By: Paul M. Pedersen,Lucie Thibault

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Contemporary Sport Management

By: Paul M. Pedersen,Lucie Thibault

Contemporary Sport Management returns with a new edition that makes this popular introductory text stronger and more applicable than ever for students who plan to enter, or are considering entering, the field of sport management. The sixth edition of Contemporary Sport Management offers the knowledge of 58 highly acclaimed contributors, 25 of them new to this work. Together, they present a wide array of cultural and educational backgrounds, offer a complete and contemporary overview of the field, and represent the diversity that is noteworthy of this profession. This latest edition offers much new and updated material: A new chapter on analytics in the sport industry New and updated international sidebars for each of the book’s 21 chapters, with accompanying questions in the web study guide New professional profiles showcasing the diversity in the field Streamlined chapters on sport management history and sociological aspects of sport management, emphasizing the issues most relevant to today’s sports managers Updated sidebars and learning features, including Historical Moment sections, chapter objectives, key terms, social media sidebars, sections on applied practice and critical thinking, and more In addition, Contemporary Sport Management offers an array of student and instructor ancillaries: A revamped web study guide that contains over 200 activities, presented through recurring features such as Day in the Life, Job Opportunities, and Learning in Action An instructor guide that houses a sample syllabus, instruction on how to use the web study guide, a section on promoting critical thinking in sport management, lecture outlines, chapter summaries, and case studies from the journal Case Studies in Sport Management to help students apply the content to real-world situations A test package and chapter quizzes that combine to offer 850 questions, in true/false, fill-in-the-blank, short answer, and multiple choice formats A presentation package of 350 slides covering the key points of each chapter, as well as an image bank of the art, tables, and content photos from the book This new edition addresses each of the common professional component topical areas that COSMA (the Commission on Sport Management Accreditation) considers essential for professional preparation: sport management foundations, functions, environment, experiential learning, and career development. Contemporary Sport Management is organized into four parts. Part I provides an overview of the field and the important leadership concepts associated with it. Part II details the major settings in which many sport management positions are carried out. In part III, readers learn about the key functional areas of sport management, including sport marketing, sport consumer behavior, sport communication, sport facility and event management, and more. And in part IV, readers examine current sport management issues, including how sport management interfaces with law, sociology, globalization, analytics, and research. Every chapter includes a section or vignette on international aspects of the field and ethics in sport management. This text particularly focuses on the ability to make principled, ethical decisions and on the ability to think critically. These two issues, of critical importance to sport managers, are examined and analyzed in detail in this book. Contemporary Sport Management, Sixth Edition, will broaden students’ understanding of sport management issues, including international issues and cultures, as it introduces them to all the aspects of the field they need to know as they prepare to enter the profession. With its up-to-date revisions and new inclusions, its internationally renowned stable of contributors, and its array of pedagogical aids, this latest edition of Contemporary Sport Management maintains its reputation as the groundbreaking and authoritative introductory text in the field.

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544

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Human Kinetics

Published Date

ISBN 10

1492588679

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9781492588672

Contemporary Sport Management 6th Edition

By: Pedersen, Paul M.,Thibault, Lucie

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Contemporary Sport Management 6th Edition

By: Pedersen, Paul M.,Thibault, Lucie

Thoroughly updated, Contemporary Sport Management, Sixth Edition, offers a complete and contemporary overview of the field. It addresses the professional component topical areas that must be mastered for COSMA accreditation, and it comes with an array of ancillaries that make instruction organized and easy.

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544

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Human Kinetics

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1492550957

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9781492550952

Regularized Image Reconstruction in Parallel MRI with MATLAB

By: Joseph Suresh Paul,Raji Susan Mathew

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Regularized Image Reconstruction in Parallel MRI with MATLAB

By: Joseph Suresh Paul,Raji Susan Mathew

Regularization becomes an integral part of the reconstruction process in accelerated parallel magnetic resonance imaging (pMRI) due to the need for utilizing the most discriminative information in the form of parsimonious models to generate high quality images with reduced noise and artifacts. Apart from providing a detailed overview and implementation details of various pMRI reconstruction methods, Regularized image reconstruction in parallel MRI with MATLAB examples interprets regularized image reconstruction in pMRI as a means to effectively control the balance between two specific types of error signals to either improve the accuracy in estimation of missing samples, or speed up the estimation process. The first type corresponds to the modeling error between acquired and their estimated values. The second type arises due to the perturbation of k-space values in autocalibration methods or sparse approximation in the compressed sensing based reconstruction model. Features: Provides details for optimizing regularization parameters in each type of reconstruction. Presents comparison of regularization approaches for each type of pMRI reconstruction. Includes discussion of case studies using clinically acquired data. MATLAB codes are provided for each reconstruction type. Contains method-wise description of adapting regularization to optimize speed and accuracy. This book serves as a reference material for researchers and students involved in development of pMRI reconstruction methods. Industry practitioners concerned with how to apply regularization in pMRI reconstruction will find this book most useful.

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289

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CRC Press

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135102924X

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9781351029247

Genetic Algorithms for Pattern Recognition

By: Sankar K. Pal,Paul P. Wang

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Genetic Algorithms for Pattern Recognition

By: Sankar K. Pal,Paul P. Wang

Solving pattern recognition problems involves an enormous amount of computational effort. By applying genetic algorithms - a computational method based on the way chromosomes in DNA recombine - these problems are more efficiently and more accurately solved. Genetic Algorithms for Pattern Recognition covers a broad range of applications in science and technology, describing the integration of genetic algorithms in pattern recognition and machine learning problems to build intelligent recognition systems. The articles, written by leading experts from around the world, accomplish several objectives: they provide insight into the theory of genetic algorithms; they develop pattern recognition theory in light of genetic algorithms; and they illustrate applications in artificial neural networks and fuzzy logic. The cross-sectional view of current research presented in Genetic Algorithms for Pattern Recognition makes it a unique text, ideal for graduate students and researchers.

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369

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CRC Press

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1351364480

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9781351364485

Problem Solving, Decision Making, and Professional Judgment

A Guide for Lawyers and Policymakers

By: Paul Brest,Linda Hamilton Krieger

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Problem Solving, Decision Making, and Professional Judgment

A Guide for Lawyers and Policymakers

By: Paul Brest,Linda Hamilton Krieger

In Problem Solving, Decision Making, and Professional Judgment, Paul Brest and Linda Hamilton Krieger have written a systematic guide to creative problem solving that prepares students to exercise effective judgment and decision making skills in the complex social environments in which they will work. The book represents a major milestone in the education of lawyers and policymakers, Developed by two leaders in the field, this first book of its type includes material drawn from statistics, decision science, social and cognitive psychology, the "judgment and decision making" (JDM) literature, and behavioral economics. It combines quantitative approaches to empirical analysis and decision making (statistics and decision science) with the psychological literature illustrating the systematic errors of the intuitive decision maker. The book can stand alone as a text or serve as a supplement to a core law or public policy curriculum. Problem Solving, Decision Making, and Professional Judgment: A Guide for Lawyers and Policymakers prepares students and professionals to be creative problem solvers, wise counselors, and effective decision makers. The authors' ultimate goals are to help readers "get it right" in their roles as professionals and citizens, and to arm them against common sources of judgment error.

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696

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Oxford University Press

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ISBN 10

0199995915

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9780199995912

Business Analytics and Statistics, 2nd Edition

By: Ken Black,John Asafu-Adjaye,Paul Burke,Nazim Khan,Gerard King,Nelson Perera,Andrew Papadimos,Carl Sherwood,Saleh Wasimi

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Business Analytics and Statistics, 2nd Edition

By: Ken Black,John Asafu-Adjaye,Paul Burke,Nazim Khan,Gerard King,Nelson Perera,Andrew Papadimos,Carl Sherwood,Saleh Wasimi

Written for the Australian and New Zealand markets, the second edition of Business Analytics & Statistics (Black et al.) presents statistics in a cutting-edge interactive digital format designed to motivate students by taking the road blocks out of self-study and to facilitate master through drill-and-skill practice.

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643

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John Wiley & Sons

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1394189672

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9781394189670

True Adventures of Richard Turpin

By: Paul Sayer

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True Adventures of Richard Turpin

By: Paul Sayer

A novel telling the true story of the life and times of Dick Turpin, 18th century highwayman, robber and murderer.

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530

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eBook Partnership

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1783013621

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9781783013623

Applied Genetic Programming and Machine Learning

By: Hitoshi Iba,Yoshihiko Hasegawa,Topon Kumar Paul

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Applied Genetic Programming and Machine Learning

By: Hitoshi Iba,Yoshihiko Hasegawa,Topon Kumar Paul

What do financial data prediction, day-trading rule development, and bio-marker selection have in common? They are just a few of the tasks that could potentially be resolved with genetic programming and machine learning techniques. Written by leaders in this field, Applied Genetic Programming and Machine Learning delineates the extension of Genetic

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354

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CRC Press

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1439803706

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9781439803707

The SAGE Dictionary of Statistics & Methodology

A Nontechnical Guide for the Social Sciences

By: W. Paul Vogt,R. Burke Johnson

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The SAGE Dictionary of Statistics & Methodology

A Nontechnical Guide for the Social Sciences

By: W. Paul Vogt,R. Burke Johnson

Written in a clear, readable style with a wide range of explanations and examples, this must-have dictionary reflects recent changes in the fields of statistics and methodology. Packed with new definitions, terms, and graphics, this invaluable resource is an ideal reference for researchers and professionals in the field and provides everything students need to read and understand a research report, including elementary terms, concepts, methodology, and design definitions, as well as concepts from qualitative research methods and terms from theory and philosophy.

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248

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SAGE Publications

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1483381781

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9781483381787

Mastering Text Mining with R

By: Ashish Kumar,Avinash Paul

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Mastering Text Mining with R

By: Ashish Kumar,Avinash Paul

Master text-taming techniques and build effective text-processing applications with R About This Book Develop all the relevant skills for building text-mining apps with R with this easy-to-follow guide Gain in-depth understanding of the text mining process with lucid implementation in the R language Example-rich guide that lets you gain high-quality information from text data Who This Book Is For If you are an R programmer, analyst, or data scientist who wants to gain experience in performing text data mining and analytics with R, then this book is for you. Exposure to working with statistical methods and language processing would be helpful. What You Will Learn Get acquainted with some of the highly efficient R packages such as OpenNLP and RWeka to perform various steps in the text mining process Access and manipulate data from different sources such as JSON and HTTP Process text using regular expressions Get to know the different approaches of tagging texts, such as POS tagging, to get started with text analysis Explore different dimensionality reduction techniques, such as Principal Component Analysis (PCA), and understand its implementation in R Discover the underlying themes or topics that are present in an unstructured collection of documents, using common topic models such as Latent Dirichlet Allocation (LDA) Build a baseline sentence completing application Perform entity extraction and named entity recognition using R In Detail Text Mining (or text data mining or text analytics) is the process of extracting useful and high-quality information from text by devising patterns and trends. R provides an extensive ecosystem to mine text through its many frameworks and packages. Starting with basic information about the statistics concepts used in text mining, this book will teach you how to access, cleanse, and process text using the R language and will equip you with the tools and the associated knowledge about different tagging, chunking, and entailment approaches and their usage in natural language processing. Moving on, this book will teach you different dimensionality reduction techniques and their implementation in R. Next, we will cover pattern recognition in text data utilizing classification mechanisms, perform entity recognition, and develop an ontology learning framework. By the end of the book, you will develop a practical application from the concepts learned, and will understand how text mining can be leveraged to analyze the massively available data on social media. Style and approach This book takes a hands-on, example-driven approach to the text mining process with lucid implementation in R.

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259

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Packt Publishing Ltd

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ISBN 10

1782174702

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9781782174707

Uncertain Judgements

Eliciting Experts' Probabilities

By: Anthony O'Hagan,Caitlin E. Buck,Alireza Daneshkhah,J. Richard Eiser,Paul H. Garthwaite,David J. Jenkinson,Jeremy E. Oakley,Tim Rakow

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Uncertain Judgements

Eliciting Experts' Probabilities

By: Anthony O'Hagan,Caitlin E. Buck,Alireza Daneshkhah,J. Richard Eiser,Paul H. Garthwaite,David J. Jenkinson,Jeremy E. Oakley,Tim Rakow

Elicitation is the process of extracting expert knowledge about some unknown quantity or quantities, and formulating that information as a probability distribution. Elicitation is important in situations, such as modelling the safety of nuclear installations or assessing the risk of terrorist attacks, where expert knowledge is essentially the only source of good information. It also plays a major role in other contexts by augmenting scarce observational data, through the use of Bayesian statistical methods. However, elicitation is not a simple task, and practitioners need to be aware of a wide range of research findings in order to elicit expert judgements accurately and reliably. Uncertain Judgements introduces the area, before guiding the reader through the study of appropriate elicitation methods, illustrated by a variety of multi-disciplinary examples. This is achieved by: Presenting a methodological framework for the elicitation of expert knowledge incorporating findings from both statistical and psychological research. Detailing techniques for the elicitation of a wide range of standard distributions, appropriate to the most common types of quantities. Providing a comprehensive review of the available literature and pointing to the best practice methods and future research needs. Using examples from many disciplines, including statistics, psychology, engineering and health sciences. Including an extensive glossary of statistical and psychological terms. An ideal source and guide for statisticians and psychologists with interests in expert judgement or practical applications of Bayesian analysis, Uncertain Judgements will also benefit decision-makers, risk analysts, engineers and researchers in the medical and social sciences.

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338

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John Wiley & Sons

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ISBN 10

0470033304

ISBN 13

9780470033302

Book's by Paul Bayes

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