LEADER 04117nam 22006615 450 001 996418278303316 005 20200704030918.0 010 $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(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$b[electronic resource] $eWith Applications in Biology and Medicine /$fby Leonhard Held, Daniel Sabanés Bové 205 $a2nd ed. 2020. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2020. 215 $a1 online resource (XIII, 402 p. 84 illus.) 225 1 $aStatistics for Biology and Health,$x1431-8776 311 $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,$x1431-8776 606 $aStatistics  606 $aBiostatistics 606 $aEcology  606 $aBiomathematics 606 $aStatistics for Life Sciences, Medicine, Health Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17030 606 $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001 606 $aBiostatistics$3https://scigraph.springernature.com/ontologies/product-market-codes/L15020 606 $aBayesian Inference$3https://scigraph.springernature.com/ontologies/product-market-codes/S18000 606 $aTheoretical Ecology/Statistics$3https://scigraph.springernature.com/ontologies/product-market-codes/L19147 606 $aGenetics and Population Dynamics$3https://scigraph.springernature.com/ontologies/product-market-codes/M31010 615 0$aStatistics . 615 0$aBiostatistics. 615 0$aEcology . 615 0$aBiomathematics. 615 14$aStatistics for Life Sciences, Medicine, Health Sciences. 615 24$aStatistical Theory and Methods. 615 24$aBiostatistics. 615 24$aBayesian Inference. 615 24$aTheoretical Ecology/Statistics. 615 24$aGenetics and Population Dynamics. 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 $a996418278303316 996 $aLikelihood and Bayesian Inference$92391152 997 $aUNISA