I have measurement data of a real experiment and a simulation modelling the same experiment. I now want to fit the simulation’s parameters, such that the simulation accurately resembles the real experiment.
The measurement data is given in form of multiple noisy time series. For simplicity let’s say the measurements are 10 time series of 100 entries each. The simulation has two parameters P,Q. For each parameter combination (P=p,Q=q) the simulation returns one time series of 100 entries.
I am struggling to tell pymc3 that I want only one single distribution for each of the two parameters, while the function evaluations are of length 100 and I have multiple reference time-series. Can someone please give me a hint or a quick demo on how to set up this scenario?