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boa: An R Package for MCMC Output Convergence Assessment and Posterior Inference

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  • Smith, Brian J.

Abstract

Markov chain Monte Carlo (MCMC) is the most widely used method of estimating joint posterior distributions in Bayesian analysis. The idea of MCMC is to iteratively produce parameter values that are representative samples from the joint posterior. Unlike frequentist analysis where iterative model fitting routines are monitored for convergence to a single point, MCMC output is monitored for convergence to a distribution. Thus, specialized diagnostic tools are needed in the Bayesian setting. To this end, the R package boa was created. This manuscript presents the user's manual for boa, which outlines the use of and methodology upon which the software is based. Included is a description of the menu system, data management capabilities, and statistical/graphical methods for convergence assessment and posterior inference. Throughout the manual, a linear regression example is used to illustrate the software.

Suggested Citation

  • Smith, Brian J., 2007. "boa: An R Package for MCMC Output Convergence Assessment and Posterior Inference," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 21(i11).
    • Handle: RePEc:jss:jstsof:v:021:i11
    DOI: http://hdl.handle.net/10.18637/jss.v021.i11
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    5. Peltonen, Jaakko & Venna, Jarkko & Kaski, Samuel, 2009. "Visualizations for assessing convergence and mixing of Markov chain Monte Carlo simulations," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4453-4470, October.
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    9. Fernández-i-Marín, Xavier, 2016. "ggmcmc: Analysis of MCMC Samples and Bayesian Inference," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(i09).
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    14. Frederic Ouedraogo & B. Wade Brorsen, 2018. "Hierarchical Bayesian Estimation of a Stochastic Plateau Response Function: Determining Optimal Levels of Nitrogen Fertilization," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 66(1), pages 87-102, March.
    15. Hong, Zhaoping & Lian, Heng, 2012. "BOPA: A Bayesian hierarchical model for outlier expression detection," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4146-4156.
    16. Fileccia, Gaetano & Sgarra, Carlo, 2018. "A particle filtering approach to oil futures price calibration and forecasting," Journal of Commodity Markets, Elsevier, vol. 9(C), pages 21-34.
    17. Peter Stüttgen & Peter Boatwright & Robert T. Monroe, 2012. "A Satisficing Choice Model," Marketing Science, INFORMS, vol. 31(6), pages 878-899, November.
    18. Burr, Deborah, 2012. "bspmma: An R Package for Bayesian Semiparametric Models for Meta-Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(i04).
    19. Al-Mamun, A. & Barber, J. & Ginting, V. & Pereira, F. & Rahunanthan, A., 2020. "Contaminant transport forecasting in the subsurface using a Bayesian framework," Applied Mathematics and Computation, Elsevier, vol. 387(C).
    20. González, Jorge & Barrientos, Andrés F. & Quintana, Fernando A., 2015. "Bayesian nonparametric estimation of test equating functions with covariates," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 222-244.

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