000			02673mam a2200325 a 4500
001			1693632
003			BD-DhUL
005			20140907150128.0
008			951003s1995 enka 000 0 eng d
020			_a0412039915
035			_a(OCoLC)ocm33233097
040			_aEUN _cEUN _dNNC _dOrLoB _dBD-DhUL
082			_a519.2 _bBAY
245	0	0	_aBayesian data analysis / _cAndrew Gelman ... [et al.]
260			_aLondon ; _aNew York : _bChapman & Hall, _cc1995.
300			_axix, 526 p. : _bill. ; _c24 cm.
365			_aUSD _b59.80
490	1		_aChapman & Hall texts in statistical science series
500			_aIncludes Index.
505	0	0	_g1. _tBackground -- _g2. _tSingle-parameter models -- _g3. _tIntroduction to multiparameter models -- _g4. _tLarge-sample inference and connections to standard statistical methods -- _g5. _tHierarchical models -- _g6. _tModel checking and sensitivity analysis -- _g7. _tStudy design in Bayesian analysis -- _g8. _tIntroduction to regression models -- _g9. _tApproximations based on posterior modes -- _g10. _tPosterior simulation and integration -- _g11. _tMarkov chain simulation -- _g12. _tModels for robust inference and sensitivity analysis -- _g13. _tHierarchical linear models -- _g14. _tGeneralized linear models -- _g15. _tMultivariate models -- _g16. _tMixture models -- _g17. _tModels for missing data -- _g18. _tConcluding advice -- _tA Standard probability distributions -- _tB Outline of proofs of asymptotic theorems.
520			_aBayesian Data Analysis is a comprehensive treatment of the statistical analysis of data from a Bayesian perspective. Modern computational tools are emphasized, and inferences are typically obtained using computer simulations.
520	8		_aThe principles of Bayesian analysis are described with an emphasis on practical rather than theoretical issues, and illustrated using actual data. A variety of models are considered, including linear regression, hierarchical (random effects) models, robust models, generalized linear models and mixture models.
520	8		_aTwo important and unique features of this text are thorough discussions of the methods for checking Bayesian models and the role of the design of data collection in influencing Bayesian statistical analysis. Issues of data collection, model formulation, computation, model checking and sensitivity analysis are all considered. The student or practising statistician will find that there is guidance on all aspects of Bayesian data analysis.
650		0	_aBayesian statistical decision theory.
650		0	_aMathematical statistics.
700	1		_aGelman, Andrew.
830		0	_aTexts in statistical science.
900			_aAUTH _bTOC
942			_2ddc _cBK
999			_c8540 _d8540