Bayesian data analysis for animal scientists : the basics

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, havin...

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Bibliographic Details
Main Author: Blasco, Agustín, (Author)
Format: eBook
Language: English
Published: Cham : Springer International Publishing, 2017.
Subjects:
Bayesian statistical decision theory.
Markov processes.
Animal industry > Statistical methods.
elektronické knihy
electronic books
ISBN: 9783319542744
9783319542737
Physical Description: 1 online resource (XVIII, 275 pages) : 62 illustrations, 57 illustrations in color

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100 1 |a Blasco, Agustín,  |e author. 
245 1 0 |a Bayesian data analysis for animal scientists :  |b the basics /  |c by Agustín Blasco. 
264 1 |a Cham :  |b Springer International Publishing,  |c 2017. 
300 |a 1 online resource (XVIII, 275 pages) :  |b 62 illustrations, 57 illustrations in color 
336 |a text  |b txt  |2 rdacontent 
337 |a počítač  |b c  |2 rdamedia 
338 |a online zdroj  |b cr  |2 rdacarrier 
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. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
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. 
504 |a Includes bibliographical references and index. 
590 |a SpringerLink  |b Springer Complete eBooks 
650 0 |a Bayesian statistical decision theory. 
650 0 |a Markov processes. 
650 0 |a Animal industry  |x Statistical methods. 
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