Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan: Including Comparisons…

Kobo ebook | April 4, 2015

byFranzi Korner-Nievergelt, Tobias Roth, Stefanie von Felten...

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Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines the Bayesian and frequentist methods of conducting data analyses. The book provides the theoretical background in an easy-to-understand approach, encouraging readers to examine the processes that generated their data. Including discussions of model selection, model checking, and multi-model inference, the book also uses effect plots that allow a natural interpretation of data. Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN introduces Bayesian software, using R for the simple modes, and flexible Bayesian software (BUGS and Stan) for the more complicated ones. Guiding the ready from easy toward more complex (real) data analyses ina step-by-step manner, the book presents problems and solutions—including all R codes—that are most often applicable to other data and questions, making it an invaluable resource for analyzing a variety of data types.



  • Introduces Bayesian data analysis, allowing users to obtain uncertainty measurements easily for any derived parameter of interest
  • Written in a step-by-step approach that allows for eased understanding by non-statisticians
  • Includes a companion website containing R-code to help users conduct Bayesian data analyses on their own data
  • All example data as well as additional functions are provided in the R-package blmeco

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From the Publisher

Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines the Bayesian and frequentist methods of conducting data analyses. The book provides the theoretical background in an easy-to-understand approach, encouraging readers to examine the processes that generated their data. Including discussions of model se...

Format:Kobo ebookPublished:April 4, 2015Publisher:Elsevier ScienceLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0128016787

ISBN - 13:9780128016787

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