This function updates the model.
modelUpdate(numUpdates, thin = 1, overRelax = FALSE)
This function updates the model by carrying out thin * numUpdates MCMC iterations for each chain.
The samples from every kth iteration will be used for inference, where k is the value of thin.
Setting thin > 1 can help to reduce the autocorrelation in the sample,
but there is no real advantage in thinning except to reduce storage requirements.
If overRelax is TRUE an over-relaxed form of MCMC (Neal, 1998)
which will be executed where possible.
This generates multiple samples at each iteration and then selects one that is
negatively correlated with the current value.
The time per iteration will be increased, but the within-chain correlations should be
reduced and hence fewer iterations may be necessary.
However, this method is not always effective and should be used with caution.
The auto-correlation function may be used to check whether the mixing of the chain is improved.
Neal, R. (1998): Suppressing random walks in Markov chain Monte Carlo using ordered over-relaxation. In M.I. Jordan (Ed.): Learning in Graphical Models, Kluwer Academic Publishers, Dordrecht, 205-230. https://glizen.com/radfordneal/publications.html
BRugs, help.WinBUGS