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100   $a20170830d2017      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aBayesian Data Analysis for Animal Scientists $eThe Basics /$fby Agustín Blasco
205   $a1st ed. 2017.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017.
215   $a1 online resource (XVIII, 275 p. 62 illus., 57 illus. in color.) 
311   $a3-319-54273-7 
320   $aIncludes bibliographical references and index.
327   $aForeword -- 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.
330   $aIn 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.
606   $aAgriculture
606   $aVeterinary medicine
606   $aBiomathematics
606   $aAnimal genetics
606   $aBiometry
606   $aAgriculture$3https://scigraph.springernature.com/ontologies/product-market-codes/L11006
606   $aVeterinary Medicine/Veterinary Science$3https://scigraph.springernature.com/ontologies/product-market-codes/H67000
606   $aMathematical and Computational Biology$3https://scigraph.springernature.com/ontologies/product-market-codes/M31000
606   $aAnimal Genetics and Genomics$3https://scigraph.springernature.com/ontologies/product-market-codes/L32030
606   $aBiostatistics$3https://scigraph.springernature.com/ontologies/product-market-codes/L15020
615  0$aAgriculture.
615  0$aVeterinary medicine.
615  0$aBiomathematics.
615  0$aAnimal genetics.
615  0$aBiometry.
615 14$aAgriculture.
615 24$aVeterinary Medicine/Veterinary Science.
615 24$aMathematical and Computational Biology.
615 24$aAnimal Genetics and Genomics.
615 24$aBiostatistics.
676   $a519.542
700   $aBlasco$b Agustín$4aut$4http://id.loc.gov/vocabulary/relators/aut$0959454
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910253937203321
996   $aBayesian Data Analysis for Animal Scientists$92174108
997   $aUNINA