I have a model that produces data given a set of parameters. Now, given data, I'ld like to find out which parameters of the model are likely. I have an implementation in Matlab that uses Delayed Rejection Adaptive Metropolis for fitting (DRAM toolbox). Basically, DRAM samples parameter values, and tries to minimize an objective function.
Not having used STAN before, I was wondering: (1) If and how such this could be implemented in STAN, and, in case it is possible, (2) if one could expect speed improvements as compared to using Matlab, since STAN code is compiled?