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

2015-04-04 Science
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Release : 2015-04-04
Genre : Science
Kind : eBook
Book Rating : 787/5 ( reviews)

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Book Synopsis Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan by : Franzi Korner-Nievergelt

Download or read book Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan written by Franzi Korner-Nievergelt. This book was released on 2015-04-04. Available in PDF, EPUB and Kindle. Book excerpt: 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

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

Bayesian statistical decision theory
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Genre : Bayesian statistical decision theory
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Book Synopsis Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN; Including Comparisons to Frequentist Statistics by :

Download or read book Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN; Including Comparisons to Frequentist Statistics written by . This book was released on . Available in PDF, EPUB and Kindle. Book excerpt:

Bayesian Models for Astrophysical Data

2017-04-27 Mathematics
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Release : 2017-04-27
Genre : Mathematics
Kind : eBook
Book Rating : 740/5 ( reviews)

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Book Synopsis Bayesian Models for Astrophysical Data by : Joseph M. Hilbe

Download or read book Bayesian Models for Astrophysical Data written by Joseph M. Hilbe. This book was released on 2017-04-27. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive guide to Bayesian methods in astronomy enables hands-on work by supplying complete R, JAGS, Python, and Stan code, to use directly or to adapt. It begins by examining the normal model from both frequentist and Bayesian perspectives and then progresses to a full range of Bayesian generalized linear and mixed or hierarchical models, as well as additional types of models such as ABC and INLA. The book provides code that is largely unavailable elsewhere and includes details on interpreting and evaluating Bayesian models. Initial discussions offer models in synthetic form so that readers can easily adapt them to their own data; later the models are applied to real astronomical data. The consistent focus is on hands-on modeling, analysis of data, and interpretations that address scientific questions. A must-have for astronomers, its concrete approach will also be attractive to researchers in the sciences more generally.

Spatial Data Analysis in Ecology and Agriculture Using R

2018-12-07 Science
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Release : 2018-12-07
Genre : Science
Kind : eBook
Book Rating : 905/5 ( reviews)

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Book Synopsis Spatial Data Analysis in Ecology and Agriculture Using R by : Richard E. Plant

Download or read book Spatial Data Analysis in Ecology and Agriculture Using R written by Richard E. Plant. This book was released on 2018-12-07. Available in PDF, EPUB and Kindle. Book excerpt: Key features: Unique in its combination of serving as an introduction to spatial statistics and to modeling agricultural and ecological data using R Provides exercises in each chapter to facilitate the book's use as a course textbook or for self-study Adds new material on generalized additive models, point pattern analysis, and new methods of Bayesian analysis of spatial data. Includes a completely revised chapter on the analysis of spatiotemporal data featuring recently introduced software and methods Updates its coverage of R software including newly introduced packages Spatial Data Analysis in Ecology and Agriculture Using R, 2nd Edition provides practical instruction on the use of the R programming language to analyze spatial data arising from research in ecology, agriculture, and environmental science. Readers have praised the book's practical coverage of spatial statistics, real-world examples, and user-friendly approach in presenting and explaining R code, aspects maintained in this update. Using data sets from cultivated and uncultivated ecosystems, the book guides the reader through the analysis of each data set, including setting research objectives, designing the sampling plan, data quality control, exploratory and confirmatory data analysis, and drawing scientific conclusions. Additional material to accompany the book, on both analyzing satellite data and on multivariate analysis, can be accessed at https://www.plantsciences.ucdavis.edu/plant/additionaltopics.htm.

Bayesian GLMs in R for Ecology

2021-10-16
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Release : 2021-10-16
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Kind : eBook
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Book Synopsis Bayesian GLMs in R for Ecology by : Mark Warren

Download or read book Bayesian GLMs in R for Ecology written by Mark Warren. This book was released on 2021-10-16. Available in PDF, EPUB and Kindle. Book excerpt: A practical handbook to introduce Bayesian general and generalised linear models (GLMs) to ecologists using R. The book is aimed at advanced undergraduate and post-graduate research students and provides access to R script and data for each analysis presented. The concepts behind Bayesian modelling are explained, along with comprehensive instructions of how to fit Bayesian models as well as highlighting the potential pitfalls to this approach.

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