I have a general query regarding informativeness of priors, since my laptops gone down and not able to run this on Stan to check (but from previous runs I think this was the case). If the priors used to specify a bayesian model are highly informative, e.g. really small standard deviations say lognormal(0.2,0.000000001) for all the parameters, will autocorrelation be non-existent because of the bias inflicted where the data virtually provides no information towards the estimation of the posterior distribution, so essentially just sampling from this prior distribution which will converge with very little autocorrelation? If this is true, why exactly does this affect autocorrelation? Pardon my lack of understanding, still getting to grips with bayesian statistics.