LEADER 03924nam 22007935 450 001 9910484137403321 005 20250701105813.0 010 $a9783662607923 010 $a3-662-60792-1 024 7 $a10.1007/978-3-662-60792-3 035 $a(CKB)4100000010770691 035 $a(DE-He213)978-3-662-60792-3 035 $a(MiAaPQ)EBC6154570 035 $a(PPN)243222955 035 $a(MiAaPQ)EBC29104010 035 $a(EXLCZ)994100000010770691 100 $a20200331d2020 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aLikelihood and bayesian Inference $ewith applications in biology and medicine /$fby Leonhard Held, Daniel Sabanés Bové 205 $aSecond edition 210 1$aBerlin, Heidelberg :$cSpringer-Verlag,$d2020. 215 $a1 online resource (XIII, 402 pages, 84 illustrations) 225 1 $aStatistics for Biology and Health,$x2197-5671 311 1 $a9783662607916 311 1 $a3-662-60791-3 330 $aThis richly illustrated textbook covers modern statistical methods with applications in medicine, epidemiology and biology. Firstly, it discusses the importance of statistical models in applied quantitative research and the central role of the likelihood function, describing likelihood-based inference from a frequentist viewpoint, and exploring the properties of the maximum likelihood estimate, the score function, the likelihood ratio and the Wald statistic. In the second part of the book, likelihood is combined with prior information to perform Bayesian inference. Topics include Bayesian updating, conjugate and reference priors, Bayesian point and interval estimates, Bayesian asymptotics and empirical Bayes methods. It includes a separate chapter on modern numerical techniques for Bayesian inference, and also addresses advanced topics, such as model choice and prediction from frequentist and Bayesian perspectives. This revised edition of the book ?Applied Statistical Inference? has been expanded to include new material on Markov models for time series analysis. It also features a comprehensive appendix covering the prerequisites in probability theory, matrix algebra, mathematical calculus, and numerical analysis, and each chapter is complemented by exercises. The text is primarily intended for graduate statistics and biostatistics students with an interest in applications. 410 0$aStatistics for Biology and Health,$x2197-5671 606 $aBiometry 606 $aStatistics 606 $aEcology 606 $aPopulation genetics 606 $aBiostatistics 606 $aStatistical Theory and Methods 606 $aBayesian Inference 606 $aTheoretical and Statistical Ecology 606 $aPopulation Genetics 606 $aEstadística bayesiana$2lemac 606 $aEstadística matemàtica$2lemac 606 $aBiometria$2lemac 606 $aEstadística mèdica$2lemac 606 $aProbabilitats$2lemac 615 0$aBiometry. 615 0$aStatistics. 615 0$aEcology. 615 0$aPopulation genetics. 615 14$aBiostatistics. 615 24$aStatistical Theory and Methods. 615 24$aBayesian Inference. 615 24$aTheoretical and Statistical Ecology. 615 24$aPopulation Genetics. 615 7$aEstadística bayesiana 615 7$aEstadística matemàtica 615 7$aBiometria 615 7$aEstadística mèdica 615 7$aProbabilitats 676 $a570.15195 700 $aHeld$b Leonhard$4aut$4http://id.loc.gov/vocabulary/relators/aut$0721217 702 $aSabanés Bové$b Daniel$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484137403321 996 $aLikelihood and Bayesian Inference$92391152 997 $aUNINA