MCMCprecision-package: MCMCprecision: Precision of discrete parameters in...

In danheck/MCMCprecision: Precision of Discrete Parameters in Transdimensional MCMC

MCMCprecision: Precision of discrete parameters in transdimensional MCMC

Description

MCMCprecision estimates the precision of the posterior model probabilities in transdimensional Markov chain Monte Carlo methods (e.g., reversible jump MCMC or product-space MCMC). This is useful for applications of transdimensional MCMC such as model selection, mixtures with varying numbers of components, change-point detection, capture-recapture models, phylogenetic trees, variable selection, and for discrete parameters in MCMC output in general.

Details

The main function to assess the estimation uncertainty of discrete MCMC output is is stationary.

The method is explained in detail in Heck et al. (2019, Statistics & Computing), available in the package by calling: vignette("MCMCprecision")

Author(s)

Daniel W. Heck

References

Heck, D. W., Overstall, A. M., Gronau, Q. F., & Wagenmakers, E.-J. (2019). Quantifying uncertainty in transdimensional Markov chain Monte Carlo using discrete Markov models. Statistics & Computing, 29, 631–643. https://dx.doi.org/10.1007/s11222-018-9828-0

See Also

Useful links:

• https://github.com/danheck/MCMCprecision

danheck/MCMCprecision documentation built on Nov. 13, 2022, 11:41 p.m.