I ran a Bayesian model that have about 2700 parameters. Among these parameters, Adaptive Metropolis algorithm was implemented to estimate ~790 parameters in the I-group and Metropolis algorithm was implemented to estimate ~115 parameters in the G-group . The model was ran with 150000 samples with thinning of 20 and 90000 burn-ins samples.The acceptance rates for the parameters in I-group and G-group were recorded when the run is completed.
Here I plotted two histograms (see below) to illustrate the acceptance rates (in ratio on the X-axis). The rates fall within [0.2,0.5] (or [20%, 50%]). Are these acceptable rates too low or high ? I notice 20% is quite low to assume that the estimated parameters are good answers or may be I don't understand the algorithms well enough to know what are good acceptance rates. Your opinions and advices are needed.