Overview
- 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
- New ways of expressing uncertainty are proposed, to help researchers derive conclusions from their experiments
- Includes supplementary material: sn.pub/extras
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Table of contents (11 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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.
Authors and Affiliations
About the author
Agustin Blasco
Professor of Animal Breeding and Genetics
Visiting scientist at ABRO (Edinburgh), INRA (Jouy en Josas) and FAO (Rome). He was President of the World Rabbit Science Association and editor in chief of the journal World Rabbit Science. His career has focused on the genetics of litter size components and genetics of meat quality in rabbits and pigs. He has published more than one hundred papers in international journals. Invited speaker several times at the European Association for Animal Production and at the World Congress on Genetics Applied to Livestock Production among others. Chapman Lecturer at the University of Wisconsin. He has taught courses on Bayesian Inference at the universities of Valencia (Spain), Edinburgh (UK), Wisconsin (USA), Padua (Italy), Sao Paulo, Lavras (Brazil), Nacional (Uruguay), Lomas (Argentina) and at INRA in Toulouse (France).
Bibliographic Information
Book Title: Bayesian Data Analysis for Animal Scientists
Book Subtitle: The Basics
Authors: Agustín Blasco
DOI: https://doi.org/10.1007/978-3-319-54274-4
Publisher: Springer Cham
eBook Packages: Biomedical and Life Sciences, Biomedical and Life Sciences (R0)
Copyright Information: Springer International Publishing AG 2017
Hardcover ISBN: 978-3-319-54273-7Published: 14 September 2017
Softcover ISBN: 978-3-319-85359-8Published: 10 August 2018
eBook ISBN: 978-3-319-54274-4Published: 30 August 2017
Edition Number: 1
Number of Pages: XVIII, 275
Number of Illustrations: 9 b/w illustrations, 151 illustrations in colour
Topics: Agriculture, Veterinary Medicine/Veterinary Science, Mathematical and Computational Biology, Animal Genetics and Genomics, Biostatistics