Hello everyone,
I need to assess whether a MCMC chain (implemented in BayPass 2.2) has reached convergence and, as far as I know, in order to check it, I must look at the posterior distribution of a chosen parameter.
Indeed, BayPass prints such a log file. Trouble is, the parameter is a matrix of post-burnin and thinned MCMC samples. So far, I've always checked MCMC convergence of a parameter either eye-metrically or by using the R package boa, but dealing with a matix-parameter I am a little in trouble.
Furthermore, I feel I'm missing some pieces in the theoretical understanding of MCMC chains, which may be hampering both my hypothesizing as well my attempt to check convergence.
If anyone can have some hints or point me to a misunderstanding of mine I would be very grateful.
Thanks in advance
Dear Andrea Modica,
I suggest you to see links and attached files on topic.
https://cran.r-project.org/web/packages/JointAI/vignettes/MCMCsettings.html
http://sbfnk.github.io/mfiidd/mcmc_diagnostics.html
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5301390/
Best regards
You can use BOA (R program for assessing the convergence of MCMC chains)
https://rdrr.io/cran/boa/
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