Suppose I fit two different family of functions (linear and polynomial function) to the same dataset using two different NumPyro models. Is there a (Bayesian) way to determine numerically which of the two models better fit my data? I’m new to Bayesian Model Selection, so any guidance will be much appreciated. Thank you!
I recommend applying the PSIS-LOO-CV approach from Vehtari, Gelman, & Gabry – implemented & documented in R as package
In Python, module ArviZ implements it – see their model comparison plots. An example of applying it, along with the ArviZ
compare() functions, from PyMC is here.
@apollostream thanks. I’m following the pymc3 approach.