000			03212cam a2200445Mu 4500
001			9780429113352
003			FlBoTFG
005			20260210180756.0
006			m d
007			cr cnu---unuuu
008			190921s2019 xx o 000 0 eng d
040			_aOCoLC-P _beng _cOCoLC-P
020			_a9781498785914
020			_a1498785913
020			_a9780429113352 _q(electronic bk.)
020			_a0429113358 _q(electronic bk.)
020			_a9780429532900 _q(electronic bk. : EPUB)
020			_a0429532903 _q(electronic bk. : EPUB)
020			_a9780429547607 _q(electronic bk. : Mobipocket)
020			_a0429547609 _q(electronic bk. : Mobipocket)
035			_a(OCoLC)1120692089
035			_a(OCoLC-P)1120692089
050		4	_aQA279.5
072		7	_aMAT _x029000 _2bisacsh
072		7	_aPBT _2bicssc
082	0	4	_a519.542 _223
100	1		_aCongdon, P.
240	1	0	_aApplied Bayesian hierarchical methods
245	1	0	_aBayesian hierarchical models : _bwith applications using R / _cPeter D. Congdon.
250			_a2nd ed.
260			_aMilton : _bCRC Press LLC, _c2019.
300			_a1 online resource (593 p.)
500			_aDescription based upon print version of record.
520			_aAn intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables, and in methods where parameters can be treated as random collections. Through illustrative data analysis and attention to statistical computing, this book facilitates practical implementation of Bayesian hierarchical methods. The new edition is a revision of the book Applied Bayesian Hierarchical Methods. It maintains a focus on applied modelling and data analysis, but now using entirely R-based Bayesian computing options. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies. This latter chapter is particularly important, due to recent advances in Bayesian computing and estimation, including the development of rjags and rstan. It also features updates throughout with new examples. The examples exploit and illustrate the broader advantages of the R computing environment, while allowing readers to explore alternative likelihood assumptions, regression structures, and assumptions on prior densities. Features: Provides a comprehensive and accessible overview of applied Bayesian hierarchical modelling Includes many real data examples to illustrate topics R code (based on rjags, jagsUI, R2OpenBUGS, and rstan) is integrated into the book, emphasizing implementation Software options and coding principles are introduced in new chapter on computing Programs and data sets available on the book's website
588			_aOCLC-licensed vendor bibliographic record.
650		7	_aMATHEMATICS / Probability & Statistics / General _2bisacsh
650		0	_aBayesian statistical decision theory. _9793
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9780429113352
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999			_c90817 _d90816