MARC Record from harvard_bibliographic_metadata

LEADER: 02904nam a22004215a 4500
001 013879498-7
005 20140103192738.0
008 131114s2014 xxu| s ||0| 0|eng d
020 $a9781461487753
020 $a9781461487753
020 $a9781461487746
024 7 $a10.1007/978-1-4614-8775-3$2doi
035 $a(Springer)9781461487753
040 $aSpringer
050 4 $aQA276-280
072 7 $aUFM$2bicssc
072 7 $aCOM077000$2bisacsh
082 04 $a519.5$223
100 1 $aKroese, Dirk P.,$eauthor.
245 10 $aStatistical Modeling and Computation /$cby Dirk P. Kroese, Joshua C.C. Chan.
264 1 $aNew York, NY :$bSpringer New York :$bImprint: Springer,$c2014.
300 $aXX, 400 p. 114 illus., 8 illus. in color.$bonline resource.
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
347 $atext file$bPDF$2rda
505 0 $aProbability Models -- Random Variables and Probability Distributions -- Joint Distributions -- Common Statistical Models -- Statistical Inference -- Likelihood -- Monte Carlo Sampling -- Bayesian Inference -- Generalized Linear Models -- Dependent Data Models -- State Space Models -- References -- Solutions -- MATLAB Primer -- Mathematical Supplement -- Index.
520 $aThis textbook on statistical modeling and statistical inference will assist advanced undergraduate and graduate students. Statistical Modeling and Computation provides a unique introduction to modern Statistics from both classical and Bayesian perspectives. It also offers an integrated treatment of Mathematical Statistics and modern statistical computation, emphasizing statistical modeling, computational techniques, and applications. Each of the three parts will cover topics essential to university courses. Part I covers the fundamentals of probability theory. In Part II, the authors introduce a wide variety of classical models that include, among others, linear regression and ANOVA models. In Part III, the authors address the statistical analysis and computation of various advanced models, such as generalized linear, state-space and Gaussian models. Particular attention is paid to fast Monte Carlo techniques for Bayesian inference on these models. Throughout the book the authors include a large number of illustrative examples and solved problems. The book also features a section with solutions, an appendix that serves as a MATLAB primer, and a mathematical supplement.
650 10 $aStatistics.
650 0 $aStatistics.
650 0 $aMathematical statistics.
650 24 $aStatistics and Computing/Statistics Programs.
650 24 $aStatistics for Life Sciences, Medicine, Health Sciences.
650 24 $aStatistical Theory and Methods.
700 1 $aC.C. Chan, Joshua,$eauthor.
776 08 $iPrinted edition:$z9781461487746
988 $a20131221
906 $0VEN