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008			140821s2011 ne a |b 001 0 eng
015			_aGBB4C8028 _2bnb
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016	7		_a016941136 _2Uk
016	7		_a016938650 _2Uk
016	7		_a017990610 _2Uk
020			_z9780124058880
020			_a9780124058880 (hbk)
035			_a(OCoLC)896850630 _z(OCoLC)899277214
040			_aUKMGB _beng _erda _cUKMGB _dOCLCO _dTEFOD _dOPELS _dYDXCP _dGZM _dUIU _dOCLCF _dOPELS _dTPH _dTEFOD _dITD _dX#7 _dU3W _dD6H _dUKMGB _dOCLCO _dBD-DhUL
050		4	_aQA279.5
082	0	4	_a519.542 _bKRD
100	1		_aKruschke, John K.
245	0	0	_aDoing Bayesian data analysis : _ba tutorial with R, JAGS, and Stan / _cJohn Kruschke.
250			_a2nd ed.
264		1	_aAmsterdam : _bAcademic Press, _c2015.
300			_axii, 759 p. : _bill. (some col.) ; _c24 cm.
336			_atext _btxt _2rdacontent
336			_astill image _bsti _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
365			_aUS$ _b89.99
500			_aIncludes index.
504			_aBibliography : p. 737-745.
505	0		_aWhat's in this book (Read this first!) -- Part I The basics: models, probability, Bayes' rule and r: Introduction: credibility, models, and parameters; The R programming language; What is this stuff called probability?; Bayes' rule -- Part II All the fundamentals applied to inferring a binomila probability: Inferring a binomial probability via exact mathematical analysis; Markov chain Monte Carlo; JAGS; Hierarchical models; Model comparison and hierarchical modeling; Null hypothesis significance testing; Bayesian approaches to testing a point ("Null") hypothesis; Goals, power, and sample size; Stan -- Part III The generalized linear model: Overview of the generalized linear model; Metric-predicted variable on one or two groups; Metric predicted variable with one metric predictor; Metric predicted variable with multiple metric predictors; Metric predicted variable with one nominal predictor; Metric predicted variable with multiple nominal predictors; Dichotomous predicted variable; Nominal predicted variable; Ordinal predicted variable; Count predicted variable; Tools in the trunk -- Bibliography -- Index.
588			_aDescription based on print version record.
650		0	_aBayesian statistical decision theory.
650		0	_aR (Computer program language).
655		4	_aElectronic books.
776	0	8	_iPrint version: Kruschke, John K. _tDoing Bayesian data analysis. _bEdition 2. _dLondon, UK ; San Diego, CA : Academic Press, [2015] _z9780124058880 _w(DLC) 2014011293 _w(OCoLC)897342420
856	4	0	_3ScienceDirect _uhttp://www.sciencedirect.com/science/book/9780124058880
942			_2ddc _cBK
999			_c247007 _d247007