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020 _a9783319853598
040 _aUNASAM BIBLIOTECA CENTRAL
_cUNASAM BIBLIOTECA CENTRAL
041 _aeng
082 _a630
_bB61
_222a ed.
100 _aBlasco, Agustín
_eautor
245 0 _aayesian data analysis for animal scientists :
_bthe basics /
_cAgustín Blasco
260 _aCham,
_bSpringer International,
_c2018
300 _axviii, 275 p. :
_bil. ;
_c17 cm.
505 _aForeword -- Notation -- 1. Do we understand classical statistics? -- 2. The Bayesian choice -- 3. Posterior distributions -- 4. MCMC -- 5. The "baby" model -- 6. The inear 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 _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.
650 _aBIOESTADISTICA
650 _aESTADISTICA BAYESIANA
650 _aPRODUCCIÓN ANIMAL
942 _cLIBRO
_2ddc
999 _c26620
_d26620