Bayesian Data Analysis for Animal Scientists

The Basics
Softcover Nachdruck of the original 1. Auflage 2017
ISBN: 978-3-319-85359-8
Verlag: Springer Nature Switzerland

Buch, Englisch, 275 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 506 g

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.

Zielgruppe

Graduate

Autoren/Hrsg.

Blasco, Agustín

Fachgebiete

• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
• Naturwissenschaften Biowissenschaften Angewandte Biologie Biomathematik
• Naturwissenschaften Agrarwissenschaften Agrarwissenschaften
• Medizin | Veterinärmedizin Veterinärmedizin Veterinärmedizin: Innere Medizin, Gynäkologie & Reproduktion
• Interdisziplinäres Wissenschaften Wissenschaften Interdisziplinär Mathematik für Naturwissenschaftler
• Naturwissenschaften Biowissenschaften Tierkunde / Zoologie Tiergenetik, Reproduktion
• Medizin | Veterinärmedizin Veterinärmedizin Veterinärmedizin

Inhaltsverzeichnis

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.

Über Autor(innen)

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).