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Bayesian Data Analysis for Animal Scientists: The Basics View larger

Bayesian Data Analysis for Animal Scientists: The Basics

9783319542737

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An easy and intuitive introduction to Bayesian methods with Monte-Carlo Markov Chain methods (MCMC).
The author uses colours to help the understanding of the formulae and numerous graphs to make the inferences more intuitive.
Examples are taken from published papers and common problems in the field of biology and agriculture.

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Publisher Springer
Author(s) Agustín Blasco
ISBN-13 9783319542737
Publication date August 2017
Edition 1st
Pages 275
Cover Hardback

In this book, we provide an easy introduction to Bayesian inference using MCMC techniques, making most topics intuitively reasonable and deriving to appendixes the more complicated matters.

The biologist or the agricultural researcher does not normally have a background in Bayesian statistics, having difficulties in following the technical books introducing Bayesian techniques. The difficulties arise from the way of making inferences, which is completely different in the Bayesian school, and from the difficulties in understanding complicated matters such as the MCMC numerical methods. We compare both schools, classic and Bayesian, underlying the advantages of Bayesian solutions, and proposing inferences based in relevant differences, guaranteed values, probabilities of similitude or the use of ratios. We also give a scope of complex problems that can be solved using Bayesian statistics, and we end the book explaining the difficulties associated to model choice and the use of small samples.

The book has a practical orientation and uses simple models to introduce the reader in this increasingly popular school of inference.

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Bayesian Data Analysis for Animal Scientists: The Basics

Bayesian Data Analysis for Animal Scientists: The Basics

An easy and intuitive introduction to Bayesian methods with Monte-Carlo Markov Chain methods (MCMC).
The author uses colours to help the understanding of the formulae and numerous graphs to make the inferences more intuitive.
Examples are taken from published papers and common problems in the field of biology and agriculture.

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