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Volumn 21, Issue 11, 2007, Pages 1-37

boa: An R package for MCMC output convergence assessment and posterior inference

Author keywords

Bayesian analysis; Convergence diagnostics; Markov chain Monte Carlo; Posterior inference; R

Indexed keywords


EID: 37349019755     PISSN: 15487660     EISSN: 15487660     Source Type: Journal    
DOI: 10.18637/jss.v021.i11     Document Type: Article
Times cited : (412)

References (20)
  • 1
    • 0005586389 scopus 로고
    • Random Polynomial Time Algorithms for Sampling from Joint Distributions
    • Technical Report 500, Carnegie-Mellon University
    • Applegate D, Kannan R, Polson NG (1990). "Random Polynomial Time Algorithms for Sampling from Joint Distributions." Technical Report 500, Carnegie-Mellon University.
    • (1990)
    • Applegate, D.1    Kannan, R.2    Polson, N.G.3
  • 2
    • 84858508805 scopus 로고    scopus 로고
    • Best N, Cowles MK, Vines K 1995, CODA: Convergence Diagnosis and Output Analysis Software for Gibbs Sampling Output, Version 0.30. MRC Biostatistics Unit, University of Cambridge, Cambridge, UK. URL
    • Best N, Cowles MK, Vines K (1995). CODA: Convergence Diagnosis and Output Analysis Software for Gibbs Sampling Output, Version 0.30. MRC Biostatistics Unit, University of Cambridge, Cambridge, UK. URL http://citeseer.ist.psu.edu/best97coda.html.
  • 3
    • 0032273615 scopus 로고    scopus 로고
    • General Methods for Monitoring Convergence of Iterative Simulations
    • Brooks S, Gelman A (1998). "General Methods for Monitoring Convergence of Iterative Simulations." Journal of Computational and Graphical Statistics, 7(4), 434-455.
    • (1998) Journal of Computational and Graphical Statistics , vol.7 , Issue.4 , pp. 434-455
    • Brooks, S.1    Gelman, A.2
  • 4
    • 0043096621 scopus 로고    scopus 로고
    • Convergence Assessment Techniques for Markov Chain Monte Carlo
    • Brooks SP, Roberts GO (1998). "Convergence Assessment Techniques for Markov Chain Monte Carlo." Statistics and Computing, 8(4), 319-335.
    • (1998) Statistics and Computing , vol.8 , Issue.4 , pp. 319-335
    • Brooks, S.P.1    Roberts, G.O.2
  • 6
    • 84972492387 scopus 로고
    • Inference from Iterative Simulation Using Multiple Sequences
    • Gelman A, Rubin DB (1992). "Inference from Iterative Simulation Using Multiple Sequences." Statistical Science, 7, 457-511.
    • (1992) Statistical Science , vol.7 , pp. 457-511
    • Gelman, A.1    Rubin, D.B.2
  • 7
    • 0344521704 scopus 로고
    • chapter Evaluating the Accuracy of Sampling-Based Approaches to Calculating Posterior Moments. Oxford University Press, New York
    • Geweke J (1992). Bayesian Statistics, volume 4, chapter Evaluating the Accuracy of Sampling-Based Approaches to Calculating Posterior Moments. Oxford University Press, New York.
    • (1992) Bayesian Statistics , vol.4
    • Geweke, J.1
  • 8
    • 0000324169 scopus 로고
    • Adaptive Rejection Sampling for Gibbs Sampling
    • Gilks WR, Wild P (1992). "Adaptive Rejection Sampling for Gibbs Sampling." Applied Statistics, 41(2), 337-348.
    • (1992) Applied Statistics , vol.41 , Issue.2 , pp. 337-348
    • Gilks, W.R.1    Wild, P.2
  • 9
    • 77956890234 scopus 로고
    • Monte Carlo Sampling Methods Using Markov Chains and their Applications
    • Hastings WK (1970). "Monte Carlo Sampling Methods Using Markov Chains and their Applications." Biometrika, 57, 97-109.
    • (1970) Biometrika , vol.57 , pp. 97-109
    • Hastings, W.K.1
  • 10
    • 0020850136 scopus 로고
    • Simulation Run Length Control in the Presence of an Initial Transient
    • Heidelberger P, Welch P (1983). "Simulation Run Length Control in the Presence of an Initial Transient." Operations Research, 31, 1109-1144.
    • (1983) Operations Research , vol.31 , pp. 1109-1144
    • Heidelberger, P.1    Welch, P.2
  • 11
    • 0002628823 scopus 로고
    • Discussion of Bayesian Computation via the Gibbs Sampler and Related Markov Chain Monte Carlo Methods
    • Jennison C (1993). "Discussion of "Bayesian Computation via the Gibbs Sampler and Related Markov Chain Monte Carlo Methods"." Journal of the Royal Statistical Society B, 55, 54-56.
    • (1993) Journal of the Royal Statistical Society B , vol.55 , pp. 54-56
    • Jennison, C.1
  • 12
    • 1642370803 scopus 로고    scopus 로고
    • Slice Sampling
    • Neal RM (2003). "Slice Sampling." Annals of Statistics, 31, 705-767.
    • (2003) Annals of Statistics , vol.31 , pp. 705-767
    • Neal, R.M.1
  • 13
    • 37349127843 scopus 로고    scopus 로고
    • Plummer M, Best N, Cowles K, Vines K (2006). coda: Convergence Diagnosis and Output Analysis for MCMC. R News, 6(1), 7-11. URL http://CRAN.R-project.org/doc/Rnews/.
    • Plummer M, Best N, Cowles K, Vines K (2006). "coda: Convergence Diagnosis and Output Analysis for MCMC." R News, 6(1), 7-11. URL http://CRAN.R-project.org/doc/Rnews/.
  • 14
    • 0344521704 scopus 로고
    • chapter How Many Iterations in the Gibbs Sampler? Oxford University Press, New York
    • Raftery AL, Lewis S (1992). Bayesian Statistics, volume 4, chapter How Many Iterations in the Gibbs Sampler? Oxford University Press, New York.
    • (1992) Bayesian Statistics , vol.4
    • Raftery, A.L.1    Lewis, S.2
  • 15
    • 33748324384 scopus 로고    scopus 로고
    • R Development Core Team , R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL
    • R Development Core Team (2006). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http: //www.R-project.org/.
    • (2006) R: A Language and Environment for Statistical Computing
  • 16
    • 0020130305 scopus 로고
    • Detecting Initial Bias in Simulation Output
    • Schruben LW (1982). "Detecting Initial Bias in Simulation Output." Operation Research, 30, 569-590.
    • (1982) Operation Research , vol.30 , pp. 569-590
    • Schruben, L.W.1
  • 17
    • 0020848271 scopus 로고
    • Optimal Tests for Initialization Bias in Simulation Output
    • Schruben LW, Signh H, Tierney L (1983). "Optimal Tests for Initialization Bias in Simulation Output." Operations Research, 31, 1167-1178.
    • (1983) Operations Research , vol.31 , pp. 1167-1178
    • Schruben, L.W.1    Signh, H.2    Tierney, L.3
  • 18
    • 37349090761 scopus 로고    scopus 로고
    • Spiegelhalter D, Thomas A, Best N, Gilks W (1996). BUGS 0.5 Bayesian Inference Using Gibbs Sampling Manual. MRC Biostatistics Unit, Institute of Public Health, Cambridge, UK, version ii edition.
    • Spiegelhalter D, Thomas A, Best N, Gilks W (1996). BUGS 0.5 Bayesian Inference Using Gibbs Sampling Manual. MRC Biostatistics Unit, Institute of Public Health, Cambridge, UK, version ii edition.
  • 19
    • 0006407254 scopus 로고    scopus 로고
    • WinBUGS - A Bayesian Modelling Framework: Concepts, Structure, and Extensibility
    • Thomas A, Best N, Spiegelhalter D (2000). "WinBUGS - A Bayesian Modelling Framework: Concepts, Structure, and Extensibility." Statistics and Computing, 10(4), 325-337.
    • (2000) Statistics and Computing , vol.10 , Issue.4 , pp. 325-337
    • Thomas, A.1    Best, N.2    Spiegelhalter, D.3
  • 20
    • 34248327024 scopus 로고    scopus 로고
    • Making BUGS Open
    • URL
    • Thomas A, O'Hara B, Ligges U, Sturtz S (2006). "Making BUGS Open." R News, 6(1), 12-17. URL http://CRAN.R-project.org/ doc/Rnews/.
    • (2006) R News , vol.6 , Issue.1 , pp. 12-17
    • Thomas, A.1    O'Hara, B.2    Ligges, U.3    Sturtz, S.4


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