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

Reseña del editor:

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

Reseña del editor:

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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