bayesian data analysis in ecology using linear models with r bugs and stan

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Bayesian Data Analysis in Ecology Using Linear Models with R  BUGS  and Stan
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Publisher : Academic Press
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ISBN 10 : 0128016787
Pages : 328 pages
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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

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

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,

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

Download or read online Bayesian Data Analysis in Ecology Using Linear Models with R BUGS and STAN Including Comparisons to Frequentist Statistics written by Anonim, published by Unknown which was released on . Get Bayesian Data Analysis in Ecology Using Linear Models with R BUGS and STAN Including Comparisons to Frequentist

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Applied Hierarchical Modeling in Ecology: Distribution, Abundance, Species Richness offers a new synthesis of the state-of-the-art of hierarchical models for plant and animal distribution, abundance, and community characteristics such as species richness using data collected in metapopulation designs. These types of data are extremely widespread in ecology and its applications

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Mixture models have been around for over 150 years, and they are found in many branches of statistical modelling, as a versatile and multifaceted tool. They can be applied to a wide range of data: univariate or multivariate, continuous or categorical, cross-sectional, time series, networks, and much more. Mixture analysis is

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Protection Strategy against Spruce Budworm

Spruce budworm (Choristoneura fumiferana (Clem.)) outbreaks are a dominant natural disturbance in the forests of Canada and northeastern USA. Widespread, severe defoliation by this native insect results in large-scale mortality and growth reductions of spruce (Picea sp.) and balsam fir (Abies balsamea (L.) Mill.) forests, and largely determines future age–

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A proven textbook based on materials developed over the last decade to teach linear, generalized, and mixed model analysis to students of ecology, evolution, and environmental studies. While R is used throughout, the focus is firmly on statistical analysis.

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The goal of this eBook is to shed light on the non-linguistic causes of language diversity, and in particular, to explore the possibility that some aspects of the structure of languages may result from an adaptation to the natural and/or human-made environment. Traditionally, language diversity has been claimed to

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Introduction to WinBUGS for Ecologists

Introduction to WinBUGS for Ecologists introduces applied Bayesian modeling to ecologists using the highly acclaimed, free WinBUGS software. It offers an understanding of statistical models as abstract representations of the various processes that give rise to a data set. Such an understanding is basic to the development of inference models

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Introduction and background; Exploratory data analysis and graphics; Deterministic functions for ecological modeling; Probability and stochastic distributions for ecological modeling; Stochatsic simulation and power analysis; Likelihood and all that; Optimization and all that; Likelihood examples; Standar statistics revisited; Modeling variance; Dynamic models.

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