Flexible Bayesian Regression Modelling

Flexible Bayesian Regression Modelling
Author: Yanan Fan,David Nott,Mike S. Smith,Jean-Luc Dortet-Bernadet
Publsiher: Academic Press
Total Pages: 302
Release: 2019-10-30
ISBN 10: 0128158638
ISBN 13: 9780128158630
Language: EN, FR, DE, ES & NL

Flexible Bayesian Regression Modelling Book Review:

Flexible Bayesian Regression Modeling is a step-by-step guide to the Bayesian revolution in regression modeling, for use in advanced econometric and statistical analysis where datasets are characterized by complexity, multiplicity, and large sample sizes, necessitating the need for considerable flexibility in modeling techniques. It reviews three forms of flexibility: methods which provide flexibility in their error distribution; methods which model non-central parts of the distribution (such as quantile regression); and finally models that allow the mean function to be flexible (such as spline models). Each chapter discusses the key aspects of fitting a regression model. R programs accompany the methods. This book is particularly relevant to non-specialist practitioners with intermediate mathematical training seeking to apply Bayesian approaches in economics, biology, finance, engineering and medicine. Introduces powerful new nonparametric Bayesian regression techniques to classically trained practitioners Focuses on approaches offering both superior power and methodological flexibility Supplemented with instructive and relevant R programs within the text Covers linear regression, nonlinear regression and quantile regression techniques Provides diverse disciplinary case studies for correlation and optimization problems drawn from Bayesian analysis ‘in the wild’

Flexible Bayesian Models for Medical Diagnostic Data

Flexible Bayesian Models for Medical Diagnostic Data
Author: Vanda Inácio de Carvalho,Miguel Brás de Carvalho,Wesley O. Johnson,Adam Branscum
Publsiher: Chapman and Hall/CRC
Total Pages: 250
Release: 2016-05-15
ISBN 10: 9781466580398
ISBN 13: 1466580399
Language: EN, FR, DE, ES & NL

Flexible Bayesian Models for Medical Diagnostic Data Book Review:

Offering a detailed and careful explanation of the methods, this book delineates Bayesian non parametric techniques to be used in health care and the statistical evaluation of diagnostic tests to determine accuracy before mass use in practice. Unique to these methods is the incorporation of prior information and elimination of subjective beliefs and asymptotic results. It includes examples such as ROC curves and ROC surfaces estimation, modeling of multivariate diagnostic data, absence of a perfect test, ROC regression methodology, and sample size determination.

Topics in Identification Limited Dependent Variables Partial Observability Experimentation and Flexible Modeling

Topics in Identification  Limited Dependent Variables  Partial Observability  Experimentation  and Flexible Modeling
Author: Ivan Jeliazkov,Justin Tobias
Publsiher: Emerald Group Publishing
Total Pages: 272
Release: 2019-10-18
ISBN 10: 1838674217
ISBN 13: 9781838674212
Language: EN, FR, DE, ES & NL

Topics in Identification Limited Dependent Variables Partial Observability Experimentation and Flexible Modeling Book Review:

Volume 40B of Advances in Econometrics examines innovations in stochastic frontier analysis, nonparametric and semiparametric modeling and estimation, A/B experiments, big-data analysis, and quantile regression.

Data Analysis and Applications 1

Data Analysis and Applications 1
Author: Christos H. Skiadas,James R. Bozeman
Publsiher: John Wiley & Sons
Total Pages: 286
Release: 2019-03-04
ISBN 10: 1119597579
ISBN 13: 9781119597575
Language: EN, FR, DE, ES & NL

Data Analysis and Applications 1 Book Review:

This series of books collects a diverse array of work that provides the reader with theoretical and applied information on data analysis methods, models, and techniques, along with appropriate applications. Volume 1 begins with an introductory chapter by Gilbert Saporta, a leading expert in the field, who summarizes the developments in data analysis over the last 50 years. The book is then divided into three parts: Part 1 presents clustering and regression cases; Part 2 examines grouping and decomposition, GARCH and threshold models, structural equations, and SME modeling; and Part 3 presents symbolic data analysis, time series and multiple choice models, modeling in demography, and data mining.

Bayesian Hierarchical Models

Bayesian Hierarchical Models
Author: Peter D. Congdon
Publsiher: CRC Press
Total Pages: 580
Release: 2019-09-16
ISBN 10: 1498785913
ISBN 13: 9781498785914
Language: EN, FR, DE, ES & NL

Bayesian Hierarchical Models Book Review:

An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables, and in methods where parameters can be treated as random collections. Through illustrative data analysis and attention to statistical computing, this book facilitates practical implementation of Bayesian hierarchical methods. The new edition is a revision of the book Applied Bayesian Hierarchical Methods. It maintains a focus on applied modelling and data analysis, but now using entirely R-based Bayesian computing options. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies. This latter chapter is particularly important, due to recent advances in Bayesian computing and estimation, including the development of rjags and rstan. It also features updates throughout with new examples. The examples exploit and illustrate the broader advantages of the R computing environment, while allowing readers to explore alternative likelihood assumptions, regression structures, and assumptions on prior densities. Features: Provides a comprehensive and accessible overview of applied Bayesian hierarchical modelling Includes many real data examples to illustrate different modelling topics R code (based on rjags, jagsUI, R2OpenBUGS, and rstan) is integrated into the book, emphasizing implementation Software options and coding principles are introduced in new chapter on computing Programs and data sets available on the book’s website

Statistical Modelling and Regression Structures

Statistical Modelling and Regression Structures
Author: Thomas Kneib,Gerhard Tutz
Publsiher: Springer Science & Business Media
Total Pages: 472
Release: 2010-01-12
ISBN 10: 3790824135
ISBN 13: 9783790824131
Language: EN, FR, DE, ES & NL

Statistical Modelling and Regression Structures Book Review:

The contributions collected in this book have been written by well-known statisticians to acknowledge Ludwig Fahrmeir's far-reaching impact on Statistics as a science, while celebrating his 65th birthday. The contributions cover broad areas of contemporary statistical model building, including semiparametric and geoadditive regression, Bayesian inference in complex regression models, time series modelling, statistical regularization, graphical models and stochastic volatility models.

Bayesian Statistics 6

Bayesian Statistics 6
Author: José M. Bernardo,James O. Berger,A. P. Dawid,Adrian F. M. Smith
Publsiher: Oxford University Press
Total Pages: 867
Release: 1999-08-12
ISBN 10: 9780198504856
ISBN 13: 0198504853
Language: EN, FR, DE, ES & NL

Bayesian Statistics 6 Book Review:

Bayesian statistics is a dynamic and fast-growing area of statistical research and the Valencia International Meetings provide the main forum for discussion. These resulting proceedings form an up-to-date collection of research.

Cognitive Computing Theory and Applications

Cognitive Computing  Theory and Applications
Author: Vijay V Raghavan,Venkat N. Gudivada,Venu Govindaraju,C.R. Rao
Publsiher: Elsevier
Total Pages: 404
Release: 2016-09-10
ISBN 10: 0444637516
ISBN 13: 9780444637512
Language: EN, FR, DE, ES & NL

Cognitive Computing Theory and Applications Book Review:

Cognitive Computing: Theory and Applications, written by internationally renowned experts, focuses on cognitive computing and its theory and applications, including the use of cognitive computing to manage renewable energy, the environment, and other scarce resources, machine learning models and algorithms, biometrics, Kernel Based Models for transductive learning, neural networks, graph analytics in cyber security, neural networks, data driven speech recognition, and analytical platforms to study the brain-computer interface. Comprehensively presents the various aspects of statistical methodology Discusses a wide variety of diverse applications and recent developments Contributors are internationally renowned experts in their respective areas

The BUGS Book

The BUGS Book
Author: David Lunn,Chris Jackson,Nicky Best,Andrew Thomas,David Spiegelhalter
Publsiher: CRC Press
Total Pages: 399
Release: 2012-10-02
ISBN 10: 1466586664
ISBN 13: 9781466586666
Language: EN, FR, DE, ES & NL

The BUGS Book Book Review:

Bayesian statistical methods have become widely used for data analysis and modelling in recent years, and the BUGS software has become the most popular software for Bayesian analysis worldwide. Authored by the team that originally developed this software, The BUGS Book provides a practical introduction to this program and its use. The text presents complete coverage of all the functionalities of BUGS, including prediction, missing data, model criticism, and prior sensitivity. It also features a large number of worked examples and a wide range of applications from various disciplines. The book introduces regression models, techniques for criticism and comparison, and a wide range of modelling issues before going into the vital area of hierarchical models, one of the most common applications of Bayesian methods. It deals with essentials of modelling without getting bogged down in complexity. The book emphasises model criticism, model comparison, sensitivity analysis to alternative priors, and thoughtful choice of prior distributions—all those aspects of the "art" of modelling that are easily overlooked in more theoretical expositions. More pragmatic than ideological, the authors systematically work through the large range of "tricks" that reveal the real power of the BUGS software, for example, dealing with missing data, censoring, grouped data, prediction, ranking, parameter constraints, and so on. Many of the examples are biostatistical, but they do not require domain knowledge and are generalisable to a wide range of other application areas. Full code and data for examples, exercises, and some solutions can be found on the book’s website.

Regression

Regression
Author: Ludwig Fahrmeir,Thomas Kneib,Stefan Lang,Brian Marx
Publsiher: Springer Science & Business Media
Total Pages: 698
Release: 2013-05-09
ISBN 10: 3642343333
ISBN 13: 9783642343339
Language: EN, FR, DE, ES & NL

Regression Book Review:

The aim of this book is an applied and unified introduction into parametric, non- and semiparametric regression that closes the gap between theory and application. The most important models and methods in regression are presented on a solid formal basis, and their appropriate application is shown through many real data examples and case studies. Availability of (user-friendly) software has been a major criterion for the methods selected and presented. Thus, the book primarily targets an audience that includes students, teachers and practitioners in social, economic, and life sciences, as well as students and teachers in statistics programs, and mathematicians and computer scientists with interests in statistical modeling and data analysis. It is written on an intermediate mathematical level and assumes only knowledge of basic probability, calculus, and statistics. The most important definitions and statements are concisely summarized in boxes. Two appendices describe required matrix algebra, as well as elements of probability calculus and statistical inference.

Bayesian Theory and Applications

Bayesian Theory and Applications
Author: Paul Damien,Petros Dellaportas,Nicholas G. Polson,David A. Stephens
Publsiher: OUP Oxford
Total Pages: 720
Release: 2013-01-24
ISBN 10: 0191647004
ISBN 13: 9780191647000
Language: EN, FR, DE, ES & NL

Bayesian Theory and Applications Book Review:

The development of hierarchical models and Markov chain Monte Carlo (MCMC) techniques forms one of the most profound advances in Bayesian analysis since the 1970s and provides the basis for advances in virtually all areas of applied and theoretical Bayesian statistics. This volume guides the reader along a statistical journey that begins with the basic structure of Bayesian theory, and then provides details on most of the past and present advances in this field. The book has a unique format. There is an explanatory chapter devoted to each conceptual advance followed by journal-style chapters that provide applications or further advances on the concept. Thus, the volume is both a textbook and a compendium of papers covering a vast range of topics. It is appropriate for a well-informed novice interested in understanding the basic approach, methods and recent applications. Because of its advanced chapters and recent work, it is also appropriate for a more mature reader interested in recent applications and developments, and who may be looking for ideas that could spawn new research. Hence, the audience for this unique book would likely include academicians/practitioners, and could likely be required reading for undergraduate and graduate students in statistics, medicine, engineering, scientific computation, business, psychology, bio-informatics, computational physics, graphical models, neural networks, geosciences, and public policy. The book honours the contributions of Sir Adrian F. M. Smith, one of the seminal Bayesian researchers, with his papers on hierarchical models, sequential Monte Carlo, and Markov chain Monte Carlo and his mentoring of numerous graduate students -the chapters are authored by prominent statisticians influenced by him. Bayesian Theory and Applications should serve the dual purpose of a reference book, and a textbook in Bayesian Statistics.

Bayesian Analysis with Python

Bayesian Analysis with Python
Author: Osvaldo Martin
Publsiher: Packt Publishing Ltd
Total Pages: 282
Release: 2016-11-25
ISBN 10: 1785889850
ISBN 13: 9781785889851
Language: EN, FR, DE, ES & NL

Bayesian Analysis with Python Book Review:

Unleash the power and flexibility of the Bayesian framework About This Book Simplify the Bayes process for solving complex statistical problems using Python; Tutorial guide that will take the you through the journey of Bayesian analysis with the help of sample problems and practice exercises; Learn how and when to use Bayesian analysis in your applications with this guide. Who This Book Is For Students, researchers and data scientists who wish to learn Bayesian data analysis with Python and implement probabilistic models in their day to day projects. Programming experience with Python is essential. No previous statistical knowledge is assumed. What You Will Learn Understand the essentials Bayesian concepts from a practical point of view Learn how to build probabilistic models using the Python library PyMC3 Acquire the skills to sanity-check your models and modify them if necessary Add structure to your models and get the advantages of hierarchical models Find out how different models can be used to answer different data analysis questions When in doubt, learn to choose between alternative models. Predict continuous target outcomes using regression analysis or assign classes using logistic and softmax regression. Learn how to think probabilistically and unleash the power and flexibility of the Bayesian framework In Detail The purpose of this book is to teach the main concepts of Bayesian data analysis. We will learn how to effectively use PyMC3, a Python library for probabilistic programming, to perform Bayesian parameter estimation, to check models and validate them. This book begins presenting the key concepts of the Bayesian framework and the main advantages of this approach from a practical point of view. Moving on, we will explore the power and flexibility of generalized linear models and how to adapt them to a wide array of problems, including regression and classification. We will also look into mixture models and clustering data, and we will finish with advanced topics like non-parametrics models and Gaussian processes. With the help of Python and PyMC3 you will learn to implement, check and expand Bayesian models to solve data analysis problems. Style and approach Bayes algorithms are widely used in statistics, machine learning, artificial intelligence, and data mining. This will be a practical guide allowing the readers to use Bayesian methods for statistical modelling and analysis using Python.

Bayesian Data Analysis Third Edition

Bayesian Data Analysis  Third Edition
Author: Andrew Gelman,John B. Carlin,Hal S. Stern,David B. Dunson,Aki Vehtari,Donald B. Rubin
Publsiher: CRC Press
Total Pages: 675
Release: 2013-11-27
ISBN 10: 1439898200
ISBN 13: 9781439898208
Language: EN, FR, DE, ES & NL

Bayesian Data Analysis Third Edition Book Review:

Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

The Oxford Handbook of Applied Bayesian Analysis

The Oxford Handbook of Applied Bayesian Analysis
Author: Anthony O' Hagan,Mike West
Publsiher: OUP Oxford
Total Pages: 928
Release: 2010-03-18
ISBN 10: 0191582824
ISBN 13: 9780191582820
Language: EN, FR, DE, ES & NL

The Oxford Handbook of Applied Bayesian Analysis Book Review:

Bayesian analysis has developed rapidly in applications in the last two decades and research in Bayesian methods remains dynamic and fast-growing. Dramatic advances in modelling concepts and computational technologies now enable routine application of Bayesian analysis using increasingly realistic stochastic models, and this drives the adoption of Bayesian approaches in many areas of science, technology, commerce, and industry. This Handbook explores contemporary Bayesian analysis across a variety of application areas. Chapters written by leading exponents of applied Bayesian analysis showcase the scientific ease and natural application of Bayesian modelling, and present solutions to real, engaging, societally important and demanding problems. The chapters are grouped into five general areas: Biomedical & Health Sciences; Industry, Economics & Finance; Environment & Ecology; Policy, Political & Social Sciences; and Natural & Engineering Sciences, and Appendix material in each touches on key concepts, models, and techniques of the chapter that are also of broader pedagogic and applied interest.

Bayesian Cognitive Modeling

Bayesian Cognitive Modeling
Author: Michael D. Lee,Eric-Jan Wagenmakers
Publsiher: Cambridge University Press
Total Pages: 280
Release: 2014-04-03
ISBN 10: 1107018455
ISBN 13: 9781107018457
Language: EN, FR, DE, ES & NL

Bayesian Cognitive Modeling Book Review:

Using a practical, hands-on approach, this book will teach anyone how to carry out Bayesian analyses and interpret the results.

Advanced Techniques for Modelling Maternal and Child Health in Africa

Advanced Techniques for Modelling Maternal and Child Health in Africa
Author: Ngianga-Bakwin Kandala,Gebrenegus Ghilagaber
Publsiher: Springer Science & Business Media
Total Pages: 330
Release: 2013-09-06
ISBN 10: 9400767781
ISBN 13: 9789400767782
Language: EN, FR, DE, ES & NL

Advanced Techniques for Modelling Maternal and Child Health in Africa Book Review:

This book presents both theoretical contributions and empirical applications of advanced statistical techniques including geo-additive models that link individual measures with area variables to account for spatial correlation; multilevel models that address the issue of clustering within family and household; multi-process models that account for interdependencies over life-course events and non-random utilization of health services; and flexible parametric alternatives to existing intensity models. These analytical techniques are illustrated mainly through modeling maternal and child health in the African context, using data from demographic and health surveys. In the past, the estimation of levels, trends and differentials in demographic and health outcomes in developing countries was heavily reliant on indirect methods that were devised to suit limited or deficient data. In recent decades, world-wide surveys like the World Fertility Survey and its successor, the Demographic and Health Survey have played an important role in filling the gap in survey data from developing countries. Such modern demographic and health surveys enable investigators to make in-depth analyses that guide policy intervention strategies, and such analyses require the modern and advanced statistical techniques covered in this book. The text is ideally suited for academics, professionals, and decision makers in the social and health sciences, as well as others with an interest in statistical modelling, demographic and health surveys. Scientists and students in applied statistics, epidemiology, medicine, social and behavioural sciences will find it of value.

Data Analysis Using Regression and Multilevel Hierarchical Models

Data Analysis Using Regression and Multilevel Hierarchical Models
Author: Andrew Gelman,Professor in the Department of Statistics Andrew Gelman,Jennifer Hill
Publsiher: Cambridge University Press
Total Pages: 625
Release: 2007
ISBN 10: 9780521686891
ISBN 13: 052168689X
Language: EN, FR, DE, ES & NL

Data Analysis Using Regression and Multilevel Hierarchical Models Book Review:

This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.

Computational Processing of the Portuguese Language

Computational Processing of the Portuguese Language
Author: A. Joaquim da Silva Teixeira,Vera Lúcia Strube de Lima,Luís Caldas de Oliveira,Paulo Quaresma
Publsiher: Springer
Total Pages: 278
Release: 2008-09-08
ISBN 10: 3540859802
ISBN 13: 9783540859802
Language: EN, FR, DE, ES & NL

Computational Processing of the Portuguese Language Book Review:

This book constitutes the thoroughly refereed proceedings of the 8th International Workshop on Computational Processing of the Portuguese Language, PROPOR 2008, held in Aveiro, Portugal, in September 2008. The 21 revised full papers and 16 revised short papers presented were carefully reviewed and selected from 63 submissions. The papers are organized in topical sections on speech analysis; ontologies, semantics and anaphora resolution; speech synthesis; machine learning applied to natural language processing; speech recognition and applications; natural language processing tools and applications; posters.

Introduction to WinBUGS for Ecologists

Introduction to WinBUGS for Ecologists
Author: Marc Kery
Publsiher: Academic Press
Total Pages: 320
Release: 2010-07-19
ISBN 10: 9780123786067
ISBN 13: 0123786061
Language: EN, FR, DE, ES & NL

Introduction to WinBUGS for Ecologists Book Review:

Introduction to WinBUGS for Ecologists introduces applied Bayesian modeling to ecologists using the highly acclaimed, free WinBUGS software. It offers an understanding of statistical models as abstract representations of the various processes that give rise to a data set. Such an understanding is basic to the development of inference models tailored to specific sampling and ecological scenarios. The book begins by presenting the advantages of a Bayesian approach to statistics and introducing the WinBUGS software. It reviews the four most common statistical distributions: the normal, the uniform, the binomial, and the Poisson. It describes the two different kinds of analysis of variance (ANOVA): one-way and two- or multiway. It looks at the general linear model, or ANCOVA, in R and WinBUGS. It introduces generalized linear model (GLM), i.e., the extension of the normal linear model to allow error distributions other than the normal. The GLM is then extended contain additional sources of random variation to become a generalized linear mixed model (GLMM) for a Poisson example and for a binomial example. The final two chapters showcase two fairly novel and nonstandard versions of a GLMM. The first is the site-occupancy model for species distributions; the second is the binomial (or N-) mixture model for estimation and modeling of abundance. Introduction to the essential theories of key models used by ecologists Complete juxtaposition of classical analyses in R and Bayesian analysis of the same models in WinBUGS Provides every detail of R and WinBUGS code required to conduct all analyses Companion Web Appendix that contains all code contained in the book and additional material (including more code and solutions to exercises)

Applied Bayesian Modelling

Applied Bayesian Modelling
Author: Peter Congdon
Publsiher: John Wiley & Sons
Total Pages: 464
Release: 2014-06-25
ISBN 10: 1118895061
ISBN 13: 9781118895061
Language: EN, FR, DE, ES & NL

Applied Bayesian Modelling Book Review:

This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets. Following in the tradition of the successful first edition, this book aims to make a wide range of statistical modeling applications accessible using tested code that can be readily adapted to the reader's own applications. The second edition has been thoroughly reworked and updated to take account of advances in the field. A new set of worked examples is included. The novel aspect of the first edition was the coverage of statistical modeling using WinBUGS and OPENBUGS. This feature continues in the new edition along with examples using R to broaden appeal and for completeness of coverage.