I am training a complex Bayesian model using Gibbs sampling and Metropolis-Hasting algorithm. Most of the parameters are directly sampled by using conjugate priors except for 3 params which are sampled by M-H. In tuning the "step size" of random walk M-H, I found making the acceptance rate to be around 0.234~0.44 will only give 1%~5% effective samples (strong autocorrelations). May I ask is the "effective size" more "important" than the acceptance rate as a criterion? And which criterion should I "ultimately" care here?