Ok, thanks Richard! I was using an approximation to the complex likelihood in my model, but this means I can just give pm.DensityDist() the custom kernel log[f(x)] that I’m working with.
Can you please clarify this paragraph :
“You can then use the custom distribution when specifying a likelihood. In this case, the values you pass are the observed values and the defined distribution takes on the role of the likelihood”
Also, given that I provide the callable function log[f(x)] to pm.DensityDist(), how exactly does PyMC3 build the log-likelihood in the sampler ?