LEADER 03894nam 22006375 450 001 9910438144503321 005 20220623193941.0 010 $a1-4614-5696-7 024 7 $a10.1007/978-1-4614-5696-4 035 $a(CKB)2670000000316172 035 $a(EBL)1081980 035 $a(OCoLC)823729019 035 $a(SSID)ssj0000878468 035 $a(PQKBManifestationID)11454311 035 $a(PQKBTitleCode)TC0000878468 035 $a(PQKBWorkID)10837262 035 $a(PQKB)10575045 035 $a(DE-He213)978-1-4614-5696-4 035 $a(MiAaPQ)EBC6311843 035 $a(MiAaPQ)EBC1081980 035 $a(Au-PeEL)EBL1081980 035 $a(CaPaEBR)ebr10983287 035 $a(PPN)168303671 035 $a(EXLCZ)992670000000316172 100 $a20130107d2013 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aApplied Bayesian Statistics$b[electronic resource] $eWith R and OpenBUGS Examples /$fby Mary Kathryn Cowles 205 $a1st ed. 2013. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2013. 215 $a1 online resource (236 p.) 225 1 $aSpringer Texts in Statistics,$x1431-875X ;$v98 300 $aDescription based upon print version of record. 311 $a1-4899-9704-0 311 $a1-4614-5695-9 327 $aWhat is Bayesian statistics? -- Review of probability -- Introduction to one-parameter models -- Inference for a population proportion -- Special considerations in Bayesian inference -- Other one-parameter models and their conjugate priors -- More realism please: Introduction to multiparameter models -- Fitting more complex Bayesian models: Markov chain Monte Carlo -- Hierarchical models, and more on convergence assessment -- Regression and hierarchical regression models -- Model Comparison, Model Checking, and Hypothesis Testing -- References -- Index. 330 $aThis book is based on over a dozen years teaching a Bayesian Statistics course. The material presented here has been used by students of different levels and disciplines, including advanced undergraduates studying Mathematics and Statistics and students in graduate programs  in Statistics, Biostatistics, Engineering, Economics, Marketing, Pharmacy, and Psychology. The goal of the book is to impart the basics of designing and carrying out Bayesian analyses, and interpreting and communicating the results.  In addition, readers will learn to use the predominant software for Bayesian model-fitting, R and OpenBUGS. The practical approach this book takes will help students of all levels to build understanding of the concepts and procedures required to answer real questions by performing Bayesian analysis of real data. Topics covered include comparing and contrasting Bayesian and classical methods, specifying hierarchical models, and assessing Markov chain Monte Carlo output. Mary Kathryn (Kate) Cowles taught Suzuki piano for many years before going to graduate school in Biostatistics.  Her research areas are Bayesian and computational statistics, with application to environmental science.  She is on the faculty of Statistics at The University of Iowa. 410 0$aSpringer Texts in Statistics,$x1431-875X ;$v98 606 $aStatistics 606 $aR (Computer program language) 606 $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001 615 0$aStatistics. 615 0$aR (Computer program language). 615 14$aStatistical Theory and Methods. 676 $a519.5 700 $aCowles$b Mary Kathryn$4aut$4http://id.loc.gov/vocabulary/relators/aut$01059794 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910438144503321 996 $aApplied Bayesian Statistics$92508216 997 $aUNINA