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006 m |o d |
007 cr |||||||||||
008 170830s2017 gw |||| o |||| 0|eng
010 _a 2019761401
020 _a9783319542737 (hbk.)
024 7 _a10.1007/978-3-319-54274-4
_2doi
035 _a(DE-He213)978-3-319-54274-4
040 _aDLC
_beng
_epn
_erda
_cDLC
072 7 _aTEC003000
_2bisacsh
072 7 _aTVB
_2bicssc
072 7 _aTVB
_2thema
082 0 4 _a630
_223
_bBLA
100 1 _aBlasco, Agustín.
_eAuthor.
_918700
245 1 0 _aBayesian data analysis for animal scientists :
_bthe basics /
_cby Agustín Blasco.
250 _a1st
260 _aCham :
_bSpringer International Publishing,
_c2017.
300 _aXVIII, 275 p.;
_b160 illustrations,151 illustrations in color.
_c24 cm.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
365 _b99.99
_cEuro
505 0 _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.
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.
588 _aDescription based on publisher-supplied MARC data.
650 0 _aAgriculture.
_9632
650 0 _aAnimal genetics.
_918701
650 0 _aBiomathematics.
_918702
650 0 _aBiostatistics.
_918703
650 0 _aVeterinary medicine.
_918704
650 1 4 _aAgriculture.
_0https://scigraph.springernature.com/ontologies/product-market-codes/L11006
_9632
650 2 4 _aAnimal Genetics and Genomics.
_0https://scigraph.springernature.com/ontologies/product-market-codes/L32030
_918705
650 2 4 _aBiostatistics.
_0https://scigraph.springernature.com/ontologies/product-market-codes/L15020
_918703
650 2 4 _aMathematical and Computational Biology.
_0https://scigraph.springernature.com/ontologies/product-market-codes/M31000
_918706
650 2 4 _aVeterinary Medicine/Veterinary Science.
_0https://scigraph.springernature.com/ontologies/product-market-codes/H67000
_918707
653 _aBayesian statistical decision theory
776 0 8 _iPrint version:
_tBayesian data analysis for animal scientists : the basics
_z9783319542737
_w(DLC) 2017945825
776 0 8 _iPrinted edition:
_z9783319542737
776 0 8 _iPrinted edition:
_z9783319542751
776 0 8 _iPrinted edition:
_z9783319853598
906 _a0
_bibc
_corigres
_du
_encip
_f20
_gy-gencatlg
942 _2ddc
_c1
_e23
_n0
999 _c411792
_d411792