Bayesian data analysis for animal scientists : the basics / by Agustín Blasco.
2017
S1-S972
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Details
Title
Bayesian data analysis for animal scientists : the basics / by Agustín Blasco.
Author
Blasco, Agustín. author.
ISBN
9783319542744
3319542745
9783319542737
3319542737
3319542745
9783319542737
3319542737
Published
Cham : Springer International Publishing : Imprint: Springer, 2017.
Language
English
Description
1 online resource (xviii, 275 pages) : illustrations.
Other Standard Identifiers
10.1007/978-3-319-54274-4 doi
Call Number
S1-S972
Dewey Decimal Classification
630
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
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Access limited to authorized users.
Digital File Characteristics
text file PDF
Available in Other Form
Print version: 9783319542737
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Table of Contents
Foreword
Notation
1. Do we understand classical statistics?
2. The Bayesian choice
3. Posterior distributions
4. MCMC
5. The "baby" model
6. The linear model. I. The "fixed" effects model
7. The linear model. II. The "mixed" model
8. A scope of the possibilities of Bayesian inference + MCMC
9. Prior information
10. Model choice
Appendix
References.
Notation
1. Do we understand classical statistics?
2. The Bayesian choice
3. Posterior distributions
4. MCMC
5. The "baby" model
6. The linear model. I. The "fixed" effects model
7. The linear model. II. The "mixed" model
8. A scope of the possibilities of Bayesian inference + MCMC
9. Prior information
10. Model choice
Appendix
References.