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Bayesian data analysis for animal sc...

Blasco, Agustin.

Bayesian data analysis for animal scientiststhe basics /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian data analysis for animal scientistsby Agustin Blasco.
Reminder of title:	the basics /
Author:	Blasco, Agustin.
Published:	Cham :Springer International Publishing :2017.
Description:	xviii, 275 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Bayesian statistical decision theory.
Online resource:	http://dx.doi.org/10.1007/978-3-319-54274-4
ISBN:	9783319542744$q(electronic bk.)

Bayesian data analysis for animal scientiststhe basics /
Blasco, Agustin.

Bayesian data analysis for animal scientiststhe basics /[electronic resource] :by Agustin Blasco. - Cham :Springer International Publishing :2017. - xviii, 275 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9783319542744$q(electronic bk.)

Standard No.: 10.1007/978-3-319-54274-4doiSubjects--Topical Terms:

182005
Bayesian statistical decision theory.

LC Class. No.: QA279.5

Dewey Class. No.: 519.542

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505 0 $a 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.
520 $a 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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650 2 4 $a Mathematical and Computational Biology. $3 514442
650 2 4 $a Animal Genetics and Genomics. $3 274341
650 2 4 $a Biostatistics. $3 339693
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