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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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    16. 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.
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