03212cam a2200445Mu 45000010014000000030008000140050017000220060019000390070015000580080041000730400026001140200018001400200015001580200036001730200033002090200043002420200040002850200049003250200046003740350022004200350024004420500012004660720025004780720016005030820016005191000016005352400042005512450083005932500012006762600036006883000031007245000052007555201612008075880047024196500062024666500047025288560072025758560102026479990017027499780429113352FlBoTFG20260210180756.0m        d        cr cnu---unuuu190921s2019    xx      o     000 0 eng d  aOCoLC-PbengcOCoLC-P  a9781498785914  a1498785913  a9780429113352q(electronic bk.)  a0429113358q(electronic bk.)  a9780429532900q(electronic bk. : EPUB)  a0429532903q(electronic bk. : EPUB)  a9780429547607q(electronic bk. : Mobipocket)  a0429547609q(electronic bk. : Mobipocket)  a(OCoLC)1120692089  a(OCoLC-P)1120692089 4aQA279.5 7aMATx0290002bisacsh 7aPBT2bicssc04a519.5422231 aCongdon, P.10aApplied Bayesian hierarchical methods10aBayesian hierarchical models :bwith applications using R /cPeter D. Congdon.  a2nd ed.  aMilton :bCRC Press LLC,c2019.  a1 online resource (593 p.)  aDescription based upon print version of record.  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  aOCLC-licensed vendor bibliographic record. 7aMATHEMATICS / Probability & Statistics / General2bisacsh 0aBayesian statistical decision theory.9793403Taylor & Francisuhttps://www.taylorfrancis.com/books/9780429113352423OCLC metadata license agreementuhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf  c90817d90816