Precision of Discrete Parameters in Transdimensional MCMC
Estimates the precision of transdimensional Markov chain Monte Carlo (MCMC) output, which is often used for Bayesian analysis of models with different dimensionality (e.g., model selection). Transdimensional MCMC (e.g., reversible jump MCMC) relies on sampling a discrete model-indicator variable to estimate the posterior model probabilities. If only few switches occur between the models, precision may be low and assessment based on the assumption of independent samples misleading. Based on the observed transition matrix of the indicator variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2017) <https://arxiv.org/abs/1703.10364> draws posterior samples of the stationary distribution to (a) assess the uncertainty in the estimated posterior model probabilities and (b) estimate the effective sample size of the MCMC output.
Rcpp, parallel, utils, stats, Matrix, combinat

Estimates the precision of transdimensional Markov chain Monte Carlo
(MCMC) output, which is often used for Bayesian analysis of models with different
dimensionality (e.g., model selection). Transdimensional MCMC (e.g., reversible
jump MCMC) relies on sampling a discrete model-indicator variable to estimate
the posterior model probabilities. If only few switches occur between the models,
precision may be low and assessment based on the assumption of independent
samples misleading. Based on the observed transition matrix of the indicator
variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2017)
<arXiv:1703.10364> draws posterior samples of the stationary
distribution to (a) assess the uncertainty in the estimated posterior model
probabilities and (b) estimate the effective sample size of the MCMC output.

Estimates the precision of transdimensional Markov chain Monte Carlo
(MCMC) output, which is often used for Bayesian analysis of models with different
dimensionality (e.g., model selection). Transdimensional MCMC (e.g., reversible
jump MCMC) relies on sampling a discrete model-indicator variable to estimate
the posterior model probabilities. If only few switches occur between the models,
precision may be low and assessment based on the assumption of independent
samples misleading. Based on the observed transition matrix of the indicator
variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2018,
Statistics & Computing) <doi:10.1007/s11222-018-9828-0> draws posterior samples
of the stationary distribution to (a) assess the uncertainty in the estimated
posterior model probabilities and (b) estimate the effective sample size of
the MCMC output.