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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.

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

A Beginner's Guide to GLM and GLMM with R

2013 Ecology
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Release : 2013
Genre : Ecology
Kind : eBook
Book Rating : 139/5 ( reviews)

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Book Synopsis A Beginner's Guide to GLM and GLMM with R by : Alain F. Zuur

Download or read book A Beginner's Guide to GLM and GLMM with R written by Alain F. Zuur. This book was released on 2013. Available in PDF, EPUB and Kindle. Book excerpt: This book presents Generalized Linear Models (GLM) and Generalized Linear Mixed Models (GLMM) based on both frequency-based and Bayesian concepts.

GLMs in R for Ecology

2019
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Release : 2019
Genre :
Kind : eBook
Book Rating : 878/5 ( reviews)

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Book Synopsis GLMs in R for Ecology by : Carl Smith

Download or read book GLMs in R for Ecology written by Carl Smith. This book was released on 2019. Available in PDF, EPUB and Kindle. Book excerpt:

Bringing Bayesian Models to Life

2019-05-15 Science
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Release : 2019-05-15
Genre : Science
Kind : eBook
Book Rating : 372/5 ( reviews)

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Book Synopsis Bringing Bayesian Models to Life by : Mevin B. Hooten

Download or read book Bringing Bayesian Models to Life written by Mevin B. Hooten. This book was released on 2019-05-15. Available in PDF, EPUB and Kindle. Book excerpt: Bringing Bayesian Models to Life empowers the reader to extend, enhance, and implement statistical models for ecological and environmental data analysis. We open the black box and show the reader how to connect modern statistical models to computer algorithms. These algorithms allow the user to fit models that answer their scientific questions without needing to rely on automated Bayesian software. We show how to handcraft statistical models that are useful in ecological and environmental science including: linear and generalized linear models, spatial and time series models, occupancy and capture-recapture models, animal movement models, spatio-temporal models, and integrated population-models. Features: R code implementing algorithms to fit Bayesian models using real and simulated data examples. A comprehensive review of statistical models commonly used in ecological and environmental science. Overview of Bayesian computational methods such as importance sampling, MCMC, and HMC. Derivations of the necessary components to construct statistical algorithms from scratch. Bringing Bayesian Models to Life contains a comprehensive treatment of models and associated algorithms for fitting the models to data. We provide detailed and annotated R code in each chapter and apply it to fit each model we present to either real or simulated data for instructional purposes. Our code shows how to create every result and figure in the book so that readers can use and modify it for their own analyses. We provide all code and data in an organized set of directories available at the authors' websites.

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