000 03299nam a22004337a 4500
001 sulb-eb0022345
003 BD-SySUS
005 20160413122247.0
007 cr nn 008mamaa
008 130107s2013 xxu| s |||| 0|eng d
020 _a9781461456964
_9978-1-4614-5696-4
024 7 _a10.1007/978-1-4614-5696-4
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aCowles, Mary Kathryn.
_eauthor.
245 1 0 _aApplied Bayesian Statistics
_h[electronic resource] :
_bWith R and OpenBUGS Examples /
_cby Mary Kathryn Cowles.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2013.
300 _aXIV, 232 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Texts in Statistics,
_x1431-875X ;
_v98
505 0 _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.
520 _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.
650 0 _aStatistics.
650 1 4 _aStatistics.
650 2 4 _aStatistical Theory and Methods.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781461456957
830 0 _aSpringer Texts in Statistics,
_x1431-875X ;
_v98
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4614-5696-4
912 _aZDB-2-SMA
942 _2Dewey Decimal Classification
_ceBooks
999 _c44437
_d44437