CODA: convergence diagnosis and output analysis for MCMC

By Martyn Plummer, N. G. Best, K. Cowles, K. Vines. 2006

3,925 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/de68accd3a2e4411c0931ca14b7eb46b44bdc9ed

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Citations in the corpus

Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7 (2018, background): ““coda” (Plummer et al. 2006) provides some of the functionality of Tracer within the R programming environment, while “AWTY” (Nylander et al.”
Bayesian analysis of biogeography when the number of areas is large. (2013, methodology): “To verify MCMC analyses converged to the same posterior distribution, we applied the Gelman diagnostic (Gelman and Rubin 1992) provided through the coda package (Plummer et al. 2006).”
Modeling, Inference and Optimization With Composable Differentiable Procedures (2016, methodology): “We use likelihood evaluations as an implementation-independent measure of computational cost and report the number of such evaluations per iteration, as well as the resulting sample efficiency (computed via R-CODA [80]), and relative speedup.”
Rank-Normalization, Folding, and Localization: An Improved Rˆ for Assessing Convergence of MCMC (with Discussion) (2019, background): “The convergence diagnostic package coda (Plummer et al., 2006) where (cid:99) var + is a marginal posterior variance estimate and B is between-chain variance estimate as given in Section 3.1.”
A Unified Framework for Fitting Bayesian Semiparametric Models to Arbitrarily Censored Survival Data, Including Spatially Referenced Data (2017, methodology): “To determine the running length of an MCMC run, one may first run a short chain without thinning, then use R packages such as coda (Plummer et al. 2006) and mcmcse (Flegal, Hughes, and Vats 2016) for convergence diagnosis and effective sample size calculations.”
Bayesian stable isotope mixing models (2012, methodology): “In all cases we run the models using the JAGS software and check convergence using the232 coda package (Plummer et al., 2006) and the Brooks-Gelman-Rubin diagnostic (Gelman and Rubin., 1992;233 Brooks and Gelman, 1998).234 235 5.1 Case study 1236 Our first case study contains 9 (δ13C, δ15N) pairs…”
Probability distribution of allometric coefficients and Bayesian estimation of aboveground tree biomass (2012, methodology): “To diagnose convergence, we used the method proposed by Gelman and Rubin (1992) implemented in the R package Coda (Plummer et al., 2006).”
Inferences and Optimal Censoring Schemes for Progressively First-Failure Censored Nadarajah-Haghighi Distribution (2020, methodology): “…computations were performed usingR statistical programming language software with mainly two useful statistical packages are ‘coda’ package proposed by Plummer et al. (2006), and ‘maxLik’ package, which using Newton-Raphson method of maximization in the computations, proposed by Henningsen and…”
MCMCpack: Markov chain Monte Carlo in R (2011, methodology): “…b0=m, B0=P, data=mydata) Using R as the home environment for MCMCpack allows us to make use of a wide range of software for MCMC convergence assessment, posterior summarization, and data visualization [such as the coda package of Plummer et al. (2006)] that is already part of the R system.”
Bayesian Updating with Structural Reliability Methods (2015, background): “A main problem of MCMC methods is that it cannot generally be ensured that the samples have reached the stationary distribution of the Markov chain, i.e. the posterior distribution (Plummer et al. 2006).”
Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models (2017, methodology): “We summarized the relationship between temperature and each trait or overall R0 by calculating the mean and 95% highest posterior density interval (HPD interval; a type of credible interval that includes the smallest continuous range containing 95% of the probability, as implemented in the coda pack”
Unbiased Markov chain Monte Carlo methods with couplings (2020, methodology): “We then divide the inefficiency by the asymptotic variance of the MCMC estimator, which is denoted by V∞, which we obtain from 106 iterations and a burn-in period of 104 by using the R package CODA (Plummer et al., 2006).”