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Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN

Including Comparisons to Frequentist Statistics - 6 - 1889208

Taschenbuch von Franzi Korner-Nievergelt , Stefanie von Felten , Tobias Roth , Bettina Almasi und Pius Korner-Nievergelt

76008258
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Details
Artikel-Nr.:
76008258
Im Sortiment seit:
06.05.2015
Erscheinungsdatum:
27.05.2015
Medium:
Taschenbuch
Einband:
Kartoniert / Broschiert
Autor:
Korner-Nievergelt, Franzi
Felten, Stefanie von
Roth, Tobias
Almasi, Bettina
Korner-Nievergelt, Pius
Verlag:
Elsevier LTD, Oxford
Sprache:
Englisch
Rubrik:
Ökologie
Seiten:
316
Abbildungen:
Illustrated
Reihe:
Academic Press
Gewicht:
615 gr
Beschreibung
Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN introduces the Bayesian and frequentist methods of conducting data analyses. The book provides the theoretical background in an easy to understand way and it encourages the reader to think about the processes that generated their data. Model selection and multi-model inference are discussed and effort is made to create effect plots that allow a much more natural interpretation of the data rather than simple parameter estimates. Model checking by graphical analysis and by posterior predictive checking is also discussed. Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN encourages readers to think about the processes that generated their data and to build models that reflect these processes as close as possible. Therefore, the Bayesian software, BUGS, JAGS, STAN and LaplacesDemon are introduced. This book guides the reader from easy towards more complex (real) data analyses, step by step. The problems and solutions, including all R codes, presented in the book are most often replicable to other data and questions.
Thus, this book can be used continually as a resource for all sorts of questions and field data collected. * Offers Bayesian data analysis, allowing users to obtain uncertainty measurements easily for any derived parameter of interest* Written in a step-by-step approach, which is accessible to non-statisticians* Includes companion website containing R-code to help users conduct Bayesian data analyses on their own data
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