000			02781cam a2200325 i 4500
999			_c19322 _d19322
001			20857861
003			OSt
005			20230201160407.0
008			190219s2019 enka b 001 0 eng
010			_a 2019934655
020			_a9780198841302 _q(paperback)
040			_aDLC _beng _cDLC _erda
042			_apcc
082			_a519.542 LUN
100	1		_aDonovan, Therese M. _q(Therese Marie), _eauthor.
245	1	0	_aBayesian statistics for beginners : _ba step-by-step approach / _cTherese M. Donovan, Ruth M. Mickey.
250			_aFirst edition.
264		1	_aOxford, United Kingdom : _bOxford University Press, _c2019.
300			_ax, 419 pages : _billustrations (chiefly color) ; _c26 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
504			_aIncludes bibliographical references (pages 399-401) and indexes.
505			_aSection 1 Basics of Probability 1: Introduction to Probability 2: Joint, Marginal, and Conditional Probability Section 2 Bayes' Theorem and Bayesian Inference 3: Bayes' Theorem 4: Bayesian Inference 5: The Author Problem - Bayesian Inference with Two Hypotheses 6: The Birthday Problem: Bayesian Inference with Multiple Discrete Hypotheses 7: The Portrait Problem: Bayesian Inference with Joint Likelihood Section 3 Probability Functions 8: Probability Mass Functions 9: Probability Density Functions Section 4 Bayesian Conjugates 10: The White House Problem: The Beta-Binomial Conjugate 11: The Shark Attack Problem: The Gamma-Poisson Conjugate 12: The Maple Syrup Problem: The Normal-Normal Conjugate Section 5 Markov Chain Monte Carlo 13: The Shark Attack Problem Revisited: MCMC with the Metropolis Algorithm 14: MCMC Diagnostic Approaches 15: The White House Problem Revisited: MCMC with the Metropolis-Hastings Algorithm 16: The Maple Syrup Problem Revisited: MCMC with Gibbs Sampling Section 6 Applications 17: The Survivor Problem: Simple Linear Regression with MCMC 18: The Survivor Problem Continued: Introduction to Bayesian Model Selection 19: The Lorax Problem: Introduction to Bayesian Networks 20: The Once-ler Problem: Introduction to Decision Trees Appendices Appendix 1: The Beta-Binomial Conjugate Solution Appendix 2: The Gamma-Poisson Conjugate Solution Appendix 3: The Normal-Normal Conjugate Solution Appendix 4: Conjugate Solutions for Simple Linear Regression Appendix 5: The Standardization of Regression Data
650		0	_aBayesian statistical decision theory.
700	1		_aMickey, Ruth M., _d1954- _eauthor.
906			_a7 _bcbc _corignew _d2 _eepcn _f20 _gy-gencatlg
942			_2ddc _cRFT