What is the practical difference between Sequential Monte Carlo sampling on its own and SMC with Approximate Bayesian Computation?
My data is similar to that shown here, in that I’m modeling the signal resulting from a biochemical reaction with a mechanistic model that has four parameters (plus time), for which I have ~50 observations over time. I’m estimating those parameters with a hierarchical Bayesian model that has ~200 parameters and ~10,000 observed points, so NUTS is… slow. It also seems a bit unreasonable, and unwise, to evaluate the likelihood of each observed point independently, so the SMC-ABC approach seems well-suited.
Is there an inherent performance difference, in computation time or accuracy, between using the
Simulator method for ABC (like this) vs writing a custom likelihood (like this) or theano opt (like this)? The
Simulator seems like the right way to go to me, but I want to make sure I’m not missing some subtleties.