Bayesian methods for statistical analysis /

Bayesian Methods for Statistical Analysis is a book on statistical methods for analysing a wide variety of data. The book consists of 12 chapters, starting with basic concepts and covering numerous topics, including Bayesian estimation, decision theory, prediction, hypothesis testing, hierarchical m...

Full description

Bibliographic Details
Corporate Author: JSTOR (Organization)
Format: eBook
Language:English
Published: Canberra : Australian Nat Univ Press, 2015.
Subjects:
Online Access:Connect to the full text of this electronic book

MARC

Tag First Indicator Second Indicator Subfields
LEADER 00000cam a22000003i 4500
001 in00003827821
006 m o d
007 cr |||||||||||
008 160408s2015 aca o 000 0 eng d
005 20260612160702.1
020 |a 1921934263  |q (electronic bk.) 
020 |a 9781921934261  |q (electronic bk.) 
020 |z 9781921934254  |q (print) 
035 |a (OCoLC)946217739 
037 |a 22573/ctt1bh26x1  |b JSTOR 
040 |a YDXCP  |b eng  |e pn  |c YDXCP  |d JSTOR  |d OCLCF  |d OCLCQ  |d AQ3  |d UtOrBLW 
049 |a TXAM 
050 4 |a QA279.5  |b .B39 2015eb 
082 0 4 |a 519.5/42  |2 23 
245 0 0 |a Bayesian methods for statistical analysis /  |c by Borek Puza. 
264 1 |a Canberra :  |b Australian Nat Univ Press,  |c 2015. 
300 |a 1 online resource 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
505 0 |a 1. Bayesian basics part 1 -- 2. Bayesian basics part 2 -- 3. Bayesian basics part 3 -- 4. Computational tools -- 5. Monte Carlo basics -- 6. MCMC methods part 1 -- 7. MCMC methods part 2 -- 8. Inference via WinBUGS -- 9. Bayesian finite population theory -- 10. Normal finite population models -- 11. Transformations and other topics -- 12. Biased sampling and nonresponse -- Appendix A: Additional exercises -- Appendix B: Distributions and notation -- Appendix C: Abbreviations and acronyms. 
520 |a Bayesian Methods for Statistical Analysis is a book on statistical methods for analysing a wide variety of data. The book consists of 12 chapters, starting with basic concepts and covering numerous topics, including Bayesian estimation, decision theory, prediction, hypothesis testing, hierarchical models, Markov chain Monte Carlo methods, finite population inference, biased sampling and nonignorable nonresponse. The book contains many exercises, all with worked solutions, including complete computer code. 
500 |a Electronic resource. 
650 0 |a Bayesian statistical decision theory.  |0 http://id.loc.gov/authorities/subjects/sh85012506 
650 7 |a Bayesian statistical decision theory.  |2 fast  |0 (OCoLC)fst00829019 
655 7 |a Electronic books.  |2 local 
710 2 |a JSTOR (Organization)  |0 http://id.loc.gov/authorities/names/no97001983 
856 4 0 |u http://proxy.library.tamu.edu/login?url=https://www.jstor.org/stable/10.2307/j.ctt1bgzbn2  |z Connect to the full text of this electronic book  |t 0 
994 |a 92  |b TXA 
999 f f |s fd003dfb-e0ca-3484-b34d-945bbf2e82d5  |i 05a20185-9878-3736-a78c-4da575156737  |t 0 
952 f f |a Texas A&M University  |b College Station  |c Electronic Resources  |s www_evans  |d Available Online  |t 0  |e QA279.5 .B39 2015eb  |h Library of Congress classification 
998 f f |a QA279.5 .B39 2015eb  |t 0  |l Available Online