000			02765cam a2200241 a 4500
008			100517s2011 flua b 001 0 eng
020			_a9781439803547
020			_a1439803544
040			_aSDU
050	0	0	_aQA279 _bB357 2011
245	0	0	_aBayesian ideas and data analysis : _ban introduction for scientists and statisticians / _cRonald Christensen ... [et al.].
260			_aBoca Raton, FL : _bCRC Press, _c2011.
300			_a498 p. : _bill.
490	1		_aChapman & Hall/CRC texts in statistical science series
505	0		_aPrologue -- Fundamental ideas I -- Integration versus simulation -- Fundamental ideas II -- Comparing populations -- Simulations -- Basic concepts of regression -- Binominal regression -- Linear regression -- Correlated data -- Count data -- Time to event data -- Time to event regression -- Binary diagnostic tests -- Nonparametric models -- Appendix A: Matrices and vectors -- Appendix B: Probability -- Appendix C: Getting started in R.
505	0		_aHKBU library
518			_aYT2025 M10
520			_a"Emphasizing the use of WinBUGS and R to analyze real data, Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians presents statistical tools to address scientific questions. It highlights foundational issues in statistics, the importance of making accurate predictions, and the need for scientists and statisticians to collaborate in analyzing data. The WinBUGS code provided offers a convenient platform to model and analyze a wide range of data. The first five chapters of the book contain core material that spans basic Bayesian ideas, calculations, and inference, including modeling one and two sample data from traditional sampling models. The text then covers Monte Carlo methods, such as Markov chain Monte Carlo (MCMC) simulation. After discussing linear structures in regression, it presents binomial regression, normal regression, analysis of variance, and Poisson regression, before extending these methods to handle correlated data. The authors also examine survival analysis and binary diagnostic testing. A complementary chapter on diagnostic testing for continuous outcomes is available on the book's website. The last chapter on nonparametric inference explores density estimation and flexible regression modeling of mean functions. The appropriate statistical analysis of data involves a collaborative effort between scientists and statisticians. Exemplifying this approach, Bayesian Ideas and Data Analysis focuses on the necessary tools and concepts for modeling and analyzing scientific data."--Publisher's description.
650		0	_aBayesian statistical decision theory.
700	1		_aChristensen, Ronald, _d1951- _9193288
900			_a= C.1 SDU
942			_cGBE _2lcc
999			_c104016 _d104016