000			03034nam a22003137a 4500
001			19630517
003			IIITD
005			20240702020003.0
008			230715b xxu||||| |||| 00| 0 eng d
010			_a 2017942214
020			_a9781473916364
035			_a(OCoLC)on1020621409
040			_aUKUOY _beng _cUKUOY _erda _dYDX _dBDX _dUKUOY _dOCLCO _dWURST _dOCLCF _dOUP _dIIITD
042			_alccopycat
050	0	0	_aQA279.5 _b.L36 2018
082	0	4	_a519.542 _223 _bLAM-S
100	1		_aLambert, Ben
245	1	2	_aA student's guide to Bayesian Statistics _cby Ben Lambert
260			_aLondon : _bSAGE, _c©2018
300			_axx, 498 p. : _bill. ; _c25 cm.
504			_aThis book includes bibliographical references and an index.
505	0		_aAn introduction to Bayesian inference -- Understanding the Bayesian formula -- Analytic Bayesian methods -- A practical guide to doing real-life Bayesian analysis: Computational Bayes -- Hierarchical models and regression. _t1: How to best use this book _t2: The subjective worlds of Frequentist and Bayesian statistics _t3: Probability - the nuts and bolts of Bayesian inference _t4: Likelihoods _t5: Priors _t6: The devil’s in the denominator _t7: The posterior - the goal of Bayesian inference _t8: An introduction to distributions for the mathematically-un-inclined _t9: Conjugate priors _t10: Evaluation of model fit and hypothesis testing _t11: Making Bayesian analysis objective? _t12: Leaving conjugates behind: Markov Chain Monte Carlo _t13: Random Walk Metropolis _t14: Gibbs sampling _t15: Hamiltonian Monte Carlo _t16: Stan _t17: Hierarchical models _t18: Linear regression models _t19: Generalised linear models and other animals _tBibliography _tIndex
520			_a"Supported by a wealth of learning features, exercises, and visual elements as well as online video tutorials and interactive simulations, this book is the first student-focused introduction to Bayesian statistics. Without sacrificing technical integrity for the sake of simplicity, the author draws upon accessible, student-friendly language to provide approachable instruction perfectly aimed at statistics and Bayesian newcomers. Through a logical structure that introduces and builds upon key concepts in a gradual way and slowly acclimatizes students to using R and Stan software, the book covers: An introduction to probability and Bayesian inference, Understanding Bayes' rule, Nuts and bolts of Bayesian analytic methods, Computational Bayes and real-world Bayesian analysis, Regression analysis and hierarchical methods. This unique guide will help students develop the statistical confidence and skills to put the Bayesian formula into practice, from the basic concepts of statistical inference to complex applications of analyses." --
650		0	_aBayesian statistical decision theory.
650		7	_aBayesian statistical decision theory. _2fast
906			_a7 _bcbc _ccopycat _d2 _encip _f20 _gy-gencatlg
942			_2ddc _cBK _03
999			_c171354 _d171354