scalings for different methods of inference

Excellent long check-in meeting with Micah Oeur (Merced) and Juan Guerra (Yale) about their summer-school projects with me and Adrian Price-Whelan (Flatiron). The projects are all performing inferences on toy data sets, where we have fake observations of a very simple dynamical system and we try to infer the parameters of that dynamical system. We are using virial theorem, Jeans modeling, Schwarzschild modeling, full forward modeling of the kinematics, and orbital torus imaging. We have results for many of these methods already (go team!) and more to come. Today we discussed the problem of measuring scalings of the inferences (the uncertainties, say) as a function of the number of observations and the quality of the data. Do they all scale the same? We also want to check sensitivity to selection effects, and wrongnesses of the assumptions.