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Author: Agustín Blasco
Publisher: Springer
ISBN: 3319542745
Category : Technology & Engineering
Languages : en
Pages : 275
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Book Description
In this book, we provide an easy introduction to Bayesian inference using MCMC techniques, making most topics intuitively reasonable and deriving to appendixes the more complicated matters. The biologist or the agricultural researcher does not normally have a background in Bayesian statistics, having difficulties in following the technical books introducing Bayesian techniques. The difficulties arise from the way of making inferences, which is completely different in the Bayesian school, and from the difficulties in understanding complicated matters such as the MCMC numerical methods. We compare both schools, classic and Bayesian, underlying the advantages of Bayesian solutions, and proposing inferences based in relevant differences, guaranteed values, probabilities of similitude or the use of ratios. We also give a scope of complex problems that can be solved using Bayesian statistics, and we end the book explaining the difficulties associated to model choice and the use of small samples. The book has a practical orientation and uses simple models to introduce the reader in this increasingly popular school of inference.
Author: Agustín Blasco
Publisher: Springer
ISBN: 3319542745
Category : Technology & Engineering
Languages : en
Pages : 275
Get Book
Book Description
In this book, we provide an easy introduction to Bayesian inference using MCMC techniques, making most topics intuitively reasonable and deriving to appendixes the more complicated matters. The biologist or the agricultural researcher does not normally have a background in Bayesian statistics, having difficulties in following the technical books introducing Bayesian techniques. The difficulties arise from the way of making inferences, which is completely different in the Bayesian school, and from the difficulties in understanding complicated matters such as the MCMC numerical methods. We compare both schools, classic and Bayesian, underlying the advantages of Bayesian solutions, and proposing inferences based in relevant differences, guaranteed values, probabilities of similitude or the use of ratios. We also give a scope of complex problems that can be solved using Bayesian statistics, and we end the book explaining the difficulties associated to model choice and the use of small samples. The book has a practical orientation and uses simple models to introduce the reader in this increasingly popular school of inference.
Author: Andrew Gelman
Publisher: CRC Press
ISBN: 1439840954
Category : Mathematics
Languages : en
Pages : 677
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Book Description
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.
Author: John Kruschke
Publisher: Academic Press
ISBN: 0124059163
Category : Mathematics
Languages : en
Pages : 776
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Book Description
Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition provides an accessible approach for conducting Bayesian data analysis, as material is explained clearly with concrete examples. Included are step-by-step instructions on how to carry out Bayesian data analyses in the popular and free software R and WinBugs, as well as new programs in JAGS and Stan. The new programs are designed to be much easier to use than the scripts in the first edition. In particular, there are now compact high-level scripts that make it easy to run the programs on your own data sets. The book is divided into three parts and begins with the basics: models, probability, Bayes’ rule, and the R programming language. The discussion then moves to the fundamentals applied to inferring a binomial probability, before concluding with chapters on the generalized linear model. Topics include metric-predicted variable on one or two groups; metric-predicted variable with one metric predictor; metric-predicted variable with multiple metric predictors; metric-predicted variable with one nominal predictor; and metric-predicted variable with multiple nominal predictors. The exercises found in the text have explicit purposes and guidelines for accomplishment. This book is intended for first-year graduate students or advanced undergraduates in statistics, data analysis, psychology, cognitive science, social sciences, clinical sciences, and consumer sciences in business. Accessible, including the basics of essential concepts of probability and random sampling Examples with R programming language and JAGS software Comprehensive coverage of all scenarios addressed by non-Bayesian textbooks: t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis) Coverage of experiment planning R and JAGS computer programming code on website Exercises have explicit purposes and guidelines for accomplishment Provides step-by-step instructions on how to conduct Bayesian data analyses in the popular and free software R and WinBugs
Author: Richard McElreath
Publisher: CRC Press
ISBN: 1315362619
Category : Mathematics
Languages : en
Pages : 488
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Book Description
Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.
Author: Miroslav Kaps
Publisher: CABI
ISBN: 1786390353
Category : Medical
Languages : en
Pages : 561
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Book Description
Designed to cover techniques for analysis of data in the animal sciences, this popular textbook provides an overview of the basic principles of statistics enabling the subsequent applications to be carried out with familiarity and understanding. Each chapter begins by introducing a problem with practical questions, followed by a brief theoretical background. Most topics are followed up with numerical examples to illustrate the methods described using data-sets from animal sciences and related fields. The same examples are then solved using the SAS software package. Written primarily for students and researchers in animal sciences, the text is also useful for those studying agricultural, biological, and veterinary sciences.
Author: Aviva Petrie
Publisher: John Wiley & Sons
ISBN: 1118567404
Category : Medical
Languages : en
Pages : 425
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Book Description
Banish your fears of statistical analysis using this clearly written and highly successful textbook. Statistics for Veterinary and Animal Science Third Edition is an introductory text which assumes no previous knowledge of statistics. It starts with very basic methodology and builds on it to encompass some of the more advanced techniques that are currently used. This book will enable you to handle numerical data and critically appraise the veterinary and animal science literature. Written in a non-mathematical way, the emphasis is on understanding the underlying concepts and correctly interpreting computer output, and not on working through mathematical formulae. Key features: Flow charts are provided to enable you to choose the correct statistical analyses in different situations Numerous real worked examples are included to help you master the procedures Two statistical packages, SPSS and Stata, are used to analyse data to familiarise you with typical computer output The data sets from the examples in the book are available as electronic files to download from the book’s companion website in ASCII, Excel, SPSS, Stata and R Workspace formats, allowing you to practice using your own software and fully get to grips with the techniques A clear indication is provided of the more advanced or obscure topics so that, if desired, you can skip them without loss of continuity. New to this edition: New chapter on reporting guidelines relevant to veterinary medicine as a ready reference for those wanting to follow best practice in planning and writing up research New chapter on critical appraisal of randomized controlled trials and observational studies in the published literature: a template is provided which is used to critically appraise two papers New chapter introducing specialist topics: ethical issues of animal investigations, spatial statistics, veterinary surveillance, and statistics in molecular and quantitative genetics Expanded glossaries of notation and terms Additional exercises and further explanations added throughout to make the book more comprehensive. Carrying out statistical procedures and interpreting the results is an integral part of veterinary and animal science. This is the only book on statistics that is specifically written for veterinary science and animal science students, researchers and practitioners.
Author: Benee F. Swindel
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 16
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Book Description
Author: Daniel Sorensen
Publisher: Springer Science & Business Media
ISBN: 0387954406
Category : Science
Languages : en
Pages : 745
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Book Description
This book, suitable for numerate biologists and for applied statisticians, provides the foundations of likelihood, Bayesian and MCMC methods in the context of genetic analysis of quantitative traits. Although a number of excellent texts in these areas have become available in recent years, the basic ideas and tools are typically described in a technically demanding style and contain much more detail than necessary. Here, an effort has been made to relate biological to statistical parameters throughout, and the book includes extensive examples that illustrate the developing argument.
Author: Jeff Gill
Publisher: CRC Press
ISBN: 1584885629
Category : Mathematics
Languages : en
Pages : 696
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Book Description
The first edition of Bayesian Methods: A Social and Behavioral Sciences Approach helped pave the way for Bayesian approaches to become more prominent in social science methodology. While the focus remains on practical modeling and basic theory as well as on intuitive explanations and derivations without skipping steps, this second edition incorporates the latest methodology and recent changes in software offerings. New to the Second Edition Two chapters on Markov chain Monte Carlo (MCMC) that cover ergodicity, convergence, mixing, simulated annealing, reversible jump MCMC, and coupling Expanded coverage of Bayesian linear and hierarchical models More technical and philosophical details on prior distributions A dedicated R package (BaM) with data and code for the examples as well as a set of functions for practical purposes such as calculating highest posterior density (HPD) intervals Requiring only a basic working knowledge of linear algebra and calculus, this text is one of the few to offer a graduate-level introduction to Bayesian statistics for social scientists. It first introduces Bayesian statistics and inference, before moving on to assess model quality and fit. Subsequent chapters examine hierarchical models within a Bayesian context and explore MCMC techniques and other numerical methods. Concentrating on practical computing issues, the author includes specific details for Bayesian model building and testing and uses the R and BUGS software for examples and exercises.
Author: Matthew L. Spangler
Publisher: Springer Nature
ISBN: 1071624601
Category : Medical
Languages : en
Pages : 421
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Book Description
This newly updated and revised volume of the Encyclopedia of Sustainability Science and Technology (ESST) details the role of Animal Breeding and Genetics in the sustainability of animal agriculture. The volume covers scientific principles and applications includes the current science used to advance cattle, poultry, swine,sheep, and equine populations, as well as the future role of techniques such as gene editing. International leaders in the field explain foundational concepts such as heritability, the covariance between relatives, statistical approaches to predicting the genetic merit of individuals, and the development and advancement of molecular techniques to elucidate changes in the DNA sequence that underly phenotypic variation. The use of genetic-based tools to improve animal agriculture and meet consumer demands across species is treated in detail. Readers will gain an understanding of how global livestock producers have implemented advanced genetic selection tools and used them to improve reproduction, production, efficiency, health, and sustainability. The interactions of genetics and production environments, and the genetic components of the complex interactions among animals are also discussed. The future of Animal Breeding and Genetics, including the challenges and opportunities that exist in feeding a growing world population, are addressed.
