When running a Bayesian analysis, one thing to check is the autocorrelation of the MCMC samples. But I don't understand what is causing this autocorrelation.
Here, they are saying that
High autocorrelation samples [from MCMC] often are caused by strong correlations among variables.
I'm wondering what are other causes of high autocorrelation samples in MCMC.
Is there a list of things to check when autocorrelation is observed in a JAGS output?
How can we manage autocorrelation in a Bayesian analysis? I know that some are saying to thin, but others are saying that it's bad. Running the model for a longer period is another solution, unfortunately costly in time and still affecting in some cases the trace of the samples in the MCMC. Why some algorithm are much more effective in exploring and being uncorrelated? Should we change the initial values for the chain to begin with?