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

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by Franzi Korner-Nievergelt & Tobias Roth & Stefanie von Felten
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Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan: Including Comparisons to Frequentist Statistics by Franzi Korner-Nievergelt & Tobias Roth & Stefanie von Felten

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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      Ebook Details
      Pages: 328
      Size: 13.8 MB
      Publisher: Academic Press
      Date published:   2015
      ISBN: 2370006539995 (DRM-EPUB)
      9780128016787 (DRM-PDF)

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    This product is listed in the following categories:

    Nonfiction > Technology > Environmental
    Nonfiction > Science > Environmental Science

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    05/23/2017
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