000 04307nam a22006135i 4500
001 978-3-319-54274-4
003 DE-He213
005 20210118122554.0
007 cr nn 008mamaa
008 170830s2017 gw | s |||| 0|eng d
020 _a9783319542744
_9978-3-319-54274-4
024 7 _a10.1007/978-3-319-54274-4
_2doi
050 4 _aS1-S972
072 7 _aTVB
_2bicssc
072 7 _aTEC003000
_2bisacsh
072 7 _aTVB
_2thema
082 0 4 _a630
_223
100 1 _aBlasco, Agustín.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aBayesian Data Analysis for Animal Scientists
_h[electronic resource] :
_bThe Basics /
_cby Agustín Blasco.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXVIII, 275 p. 160 illus., 151 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
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.
650 0 _aAgriculture.
650 0 _aVeterinary medicine.
650 0 _aBiomathematics.
650 0 _aAnimal genetics.
650 0 _aBiostatistics.
650 1 4 _aAgriculture.
_0http://scigraph.springernature.com/things/product-market-codes/L11006
650 2 4 _aVeterinary Medicine/Veterinary Science.
_0http://scigraph.springernature.com/things/product-market-codes/H67000
650 2 4 _aMathematical and Computational Biology.
_0http://scigraph.springernature.com/things/product-market-codes/M31000
650 2 4 _aAnimal Genetics and Genomics.
_0http://scigraph.springernature.com/things/product-market-codes/L32030
650 2 4 _aBiostatistics.
_0http://scigraph.springernature.com/things/product-market-codes/L15020
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319542737
776 0 8 _iPrinted edition:
_z9783319542751
776 0 8 _iPrinted edition:
_z9783319853598
856 4 0 _uhttps://doi.org/10.1007/978-3-319-54274-4
912 _aZDB-2-SBL
999 _c445711
_d445711
942 _cEB
506 _aAvailable to subscribing member institutions only. Доступно лише організаціям членам підписки.
506 _fOnline access from local network of NaUOA.
506 _fOnline access with authorization at https://link.springer.com/
506 _fОнлайн-доступ з локальної мережі НаУОА.
506 _fОнлайн доступ з авторизацією на https://link.springer.com/