



LEADER 
02183nam a2200337 u 4500 
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00000000000000.0 
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tu 
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221110 r  eng 
020 


a 9781921934254

020 


a 1921934255

050 

4 
a QA279.5

100 
1 

a Puza, Borek

245 
0 
0 
a Bayesian Methods for Statistical Analysis
h Elektronische Ressource

260 


b ANU Press
c 2015, 2015

300 


a 1 online resource

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

505 
0 

a Includes bibliographical references

653 


a Mathematics / Probability & Statistics / Bayesian Analysis

653 


a Mathematics / Probability & Statistics

653 


a Bayesian statistical decision theory

653 


a Mathematics

041 
0 
7 
a eng
2 ISO 6392

989 


b ZDB39JOA
a JSTOR Open Access Books

028 
5 
0 
a 10.26530/OAPEN_611011

776 


z 9781921934261

776 


z 1921934263

856 
4 
0 
u https://www.jstor.org/stable/10.2307/j.ctt1bgzbn2
x Verlag
3 Volltext

082 
0 

a 519.5/42

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. It is suitable for selfstudy or a semesterlong course, with three hours of lectures and one tutorial per week for 13 weeks
