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

By Brian J. Smith. 2007

641 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Statistics and Probability

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Citations in the corpus

A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use (2010, methodology): “As a practical rule of thumb, a 0.975 quantile for R̂ 1.2 is sufficient to claim convergence (Smith, 2007).”
Bayesian Mode Regression (2012, methodology): “Convergence was assessed using time series plots and the R package boa (Smith, 2007).”
Recovery of Graded Response Model Parameters (2012, methodology): “scale reduction factor (Brooks & Gelman, 1998), estimated potential scale reduction (Gelman, 1996), and half-width test (Heidelberger & Welch, 1983) were computed using BOA (Smith, 2007).”