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006			m o d
007			cr nn||||mamaa
008			170830s2017 sz a ob 001 0 eng d
020			_a3319542745
020			_a9783319542744
020			_z3319542737
020			_z9783319542737
040			_aUPM _beng _erda _epn _cUPM _dYDX _dDKU _dGW5XE _dAZU _dNOC _dOCLCQ _dMERER _dOCLCF _dIOG _dCOO _dOCLCQ _dOH1 _dJG0 _dOCLCQ _dES-MaUEC _bspa
050		4	_aQA279.5 _b2017 EB
100	1		_aBlasco, Agustín, _eautor
245	1	0	_aBayesian data analysis for animal scientists : _bthe basics _cby Agustín Blasco.
264		1	_aCham, Switzerland _bSpringer International Publishing _c2017
300			_a1 recurso en línea (XVIII, 275 páginas) : _bilustraciones (algunas a color)
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF
500			_a _bSpringer Biomedical and Life Sciences eBooks 2017 English+International
504			_aIncluye referencias bibliográficas e índice
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	3		_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		7	_aAgricultura _2 _0(OCoLC)fst00801355 _0 _9665994
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-54274-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017
998			_b02/2018 _dz _e- _zSI
999			_c96558 _d96558 _x1