000			03082nam a22004455i 4500
001			978-1-4614-8775-3
003			DE-He213
005			20140220082503.0
007			cr nn 008mamaa
008			131114s2014 xxu| s |||| 0|eng d
020			_a9781461487753 _9978-1-4614-8775-3
024	7		_a10.1007/978-1-4614-8775-3 _2doi
050		4	_aQA276-280
072		7	_aUFM _2bicssc
072		7	_aCOM077000 _2bisacsh
082	0	4	_a519.5 _223
100	1		_aKroese, Dirk P. _eauthor.
245	1	0	_aStatistical Modeling and Computation _h[electronic resource] / _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		0	_aStatistics.
650		0	_aMathematical statistics.
650	1	4	_aStatistics.
650	2	4	_aStatistics and Computing/Statistics Programs.
650	2	4	_aStatistics for Life Sciences, Medicine, Health Sciences.
650	2	4	_aStatistical Theory and Methods.
700	1		_aC.C. Chan, Joshua. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781461487746
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4614-8775-3
912			_aZDB-2-SMA
999			_c92244 _d92244