000			04104nam a22004455i 4500
001			978-0-387-35433-0
003			DE-He213
005			20160302162032.0
007			cr nn 008mamaa
008			100301s2006 xxu| s |||| 0|eng d
020			_a9780387354330 _9978-0-387-35433-0
024	7		_a10.1007/978-0-387-35433-0 _2doi
050		4	_aQA276-280
072		7	_aPBT _2bicssc
072		7	_aMAT029000 _2bisacsh
082	0	4	_a519.5 _223
100	1		_aGhosh, Jayanta K. _eauthor.
245	1	3	_aAn Introduction to Bayesian Analysis _h[electronic resource] : _bTheory and Methods / _cby Jayanta K. Ghosh, Mohan Delampady, Tapas Samanta.
264		1	_aNew York, NY : _bSpringer New York, _c2006.
300			_aXIII, 354 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
505	0		_aStatistical Preliminaries -- Bayesian Inference and Decision Theory -- Utility, Prior, and Bayesian Robustness -- Large Sample Methods -- Choice of Priors for Low-dimensional Parameters -- Hypothesis Testing and Model Selection -- Bayesian Computations -- Some Common Problems in Inference -- High-dimensional Problems -- Some Applications.
520			_aThis is a graduate-level textbook on Bayesian analysis blending modern Bayesian theory, methods, and applications. Starting from basic statistics, undergraduate calculus and linear algebra, ideas of both subjective and objective Bayesian analysis are developed to a level where real-life data can be analyzed using the current techniques of statistical computing. Advances in both low-dimensional and high-dimensional problems are covered, as well as important topics such as empirical Bayes and hierarchical Bayes methods and Markov chain Monte Carlo (MCMC) techniques. Many topics are at the cutting edge of statistical research. Solutions to common inference problems appear throughout the text along with discussion of what prior to choose. There is a discussion of elicitation of a subjective prior as well as the motivation, applicability, and limitations of objective priors. By way of important applications the book presents microarrays, nonparametric regression via wavelets as well as DMA mixtures of normals, and spatial analysis with illustrations using simulated and real data. Theoretical topics at the cutting edge include high-dimensional model selection and Intrinsic Bayes Factors, which the authors have successfully applied to geological mapping. The style is informal but clear. Asymptotics is used to supplement simulation or understand some aspects of the posterior. J.K. Ghosh has been Director and Jawaharlal Nehru Professor at the Indian Statistical Institute and President of the International Statistical Institute. He is currently a professor of statistics at Purdue University and professor emeritus at the Indian Statistical Institute. He has been the editor of Sankhya and has served on the editorial boards of several journals including the Annals of Statistics. His current interests in Bayesian analysis include asymptotics, nonparametric methods, high-dimensional model selection, reliability and survival analysis, bioinformatics, astrostatistics and sparse and not so sparse mixtures. Mohan Delampady and Tapas Samanta are both professors of statistics at the Indian Statistical Institute and both are interested in Bayesian inference, specifically in topics such as model selection, asymptotics, robustness and nonparametrics.
650		0	_aStatistics.
650	1	4	_aStatistics.
650	2	4	_aStatistical Theory and Methods.
700	1		_aDelampady, Mohan. _eauthor.
700	1		_aSamanta, Tapas. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387400846
830		0	_aSpringer Texts in Statistics, _x1431-875X
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-35433-0
912			_aZDB-2-SMA
999			_c175359 _d175359