Is there any method to check the convergence in MCMC?
I have read the Gelman-Rubin method for check the convergence in MCMC on m ≥ 2 chain, but when I work with only one chain, what can i do to check the convergence? Is there any method that works fine with m = 1 chain?
When to summarize the chain in MCMC?
For that reason, the advice is to summarize the chain only when really necessary, otherwise things like the prior predictive distribution etc. should always be created by sampling directly from the chain.
How to check for pairwise correlations in MCMC chain?
To check for pairwise correlations is quite easy – just use pairs on the MCMC chain: or use this code snippet which produces the “nicer” pair correlation plot that you can see below. panel.hist <- function ( x.)
When does the Gelman-Rubin test check convergence?
First, the Gelman-Rubin test does not check convergence of an MCMC Markov chain but simply an agreement between several parallel chains: if all chains miss a highly concentrated but equally highly important mode of the target distribution, the Gelman-Rubin criterion concludes to the convergence of the chains.
How to check stationarity on a single Markov chain?
Second, to check convergence or stationarity on a single Markov chain ( x t) t = 1, …, T, one needs to know a lot about the target distribution π ( x) because, otherwise, all you can judge from the sequence of values x 1, x 2, …, x T is their stability. Hence only the ability of the MCMC sampler to explore the current region of the support of π.
Is there a coda package for MCMC in R?
This is supported in the coda package in R (for “Output analysis and diagnostics for Markov Chain Monte Carlo simulations”). coda also includes other functions (such as the Geweke’s convergence diagnostic). You can also have a look at “boa: An R Package for MCMC Output Convergence Assessment and Posterior Inference”.