Inference from simulations and monitoring convergence

Handbook of Markov Chain Monte Carlo

Volumn , Issue , 2011, Pages 163-174

  Gelman, Andrew ^a   Shirley, Kenneth ^a  

^a Howard Hughes Medical Institute   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85059435623     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (20)

1. Brooks, S. and Gelman, A. 1998. General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics, 7:434-455
2. Brooks, S. and Giudici, P. 2000. MCMC convergence assessment via two-way ANOVA. Journal of Computational and Graphical Statistics, 9:266-285
3. Brooks, S., Giudici, P., and Phillipe, A. 2003. Nonparametric convergence assessment for MCMC model selection. Journal of Computational and Graphical Statistics, 12:1-22
4. Cowles, M. K. and Carlin, B. P. 1996. Markov chain Monte Carlo convergence diagnostics: A comparative review. Journal of the American Statistical Association, 91:883-904
5. Cook, S., Gelman,A., and Rubin, D. B. 2006. Validation of software for Bayesian models using posterior quantiles. Journal of Computational and Graphical Statistics, 15:675-692
6. Efron, B. and Tibshirani, R. 1993. An Introduction to the Bootstrap. Chapman & Hall, New York
7. Fosdick, L. D. 1959. Calculation of order parameters in a binary alloy by the Monte Carlo method. Physical Review, 116:565-573
8. Gelman, A. and Rubin, D. B. 1992. Inference from iterative simulation using multiple sequences (with discussion). Statistical Science, 7:457-511
9. Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. 2003. Bayesian Data Analysis, 2nd edition. Chapman & Hall/CRC, Boca Raton, FL
10. Geyer, C. 1998. Burn-in is unnecessary. www.stat.umn.edu/~charlie/mcmc/burn.html
11. Kass, R. E., Carlin, B. P., Gelman, A., and Neal, R. 1998. Markov chain Monte Carlo in practice: A roundtable discussion. American Statistician, 52:93-100
12. Liu, C. and Rubin, D. B. 1996. Markov-normal analysis of iterative simulations before their convergence. Journal of Econometrics, 75:69-78
13. Liu, C. and Rubin, D. B. 2002. Model-based analysis to improve the performance of iterative simulations. Statistica Sinica, 12:751-767
14. Peltonen, J., Venna, J., and Kaski, S. 2009. Visualizations for assessing convergence and mixing of Markov chain Monte Carlo simulations. Computational Statistics and Data Analysis, 53:4453-4470
15. Plummer, M., Best, N., Cowles, K., and Vines, K. 2005. Output analysis and diagnostics for MCMC: The coda package for R. http://pbil.univ-lyon1.fr/library/coda/html/00Index.html
16. Raftery, A. E. and Lewis, S. M. 1992. How many iterations in the Gibbs sampler? In J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith (eds), Bayesian Statistics 4: Proceedings of the Fourth Valencia International Meeting, pp. 765-776. Oxford University Press, Oxford
17. Shirley, K. and Gelman, A. 2010. State-level trends in public opinion about the death penalty, 1953-2006. Technical report, Department of Statistics, Columbia University
18. Spiegelhalter, D., Thomas, A., Best, N., Gilks, W., and Lunn, D. 1994, 2003. BUGS: Bayesian inference using Gibbs sampling. MRC Biostatistics Unit, Cambridge, UK. www.mrc-bsu. cam.ac.uk/bugs/
19. Sturtz, S., Ligges, U., and Gelman, A. 2005. R2WinBUGS: A package for running WinBUGS from R. Journal of Statistical Software, 12(3)
20. Venna, J., Kaski, S., and Peltonen, J. 2003. Visualizations for assessing convergence and mixing of MCMC. In N. Lavraè, D. Gamberger, H. Blockeel, and L. Todorovski (eds), Machine Learning: ECML 2003, Lecture Notes in Artificial Intelligence, Vol. 2837. Springer, Berlin