This question is sort of related to another I asked before (Observed Variable as a Range). I’m wondering if I can and should model the following situation. Each observed data point may have a different likelihood (e.g., some may be uniform, others may be skew-normal, etc.). So I’d like to use pm.Potential to have full control of which likelihood to apply to each observed value. I’m thinking of two approaches:
-
Define a single potential before the call to
pm.sample, and in the class that implements the likelihoods and extendstt.Op, I’d add up the log of all density evaluations. -
Define multiple potentials in a
forloop for each observed value before the call topm.sample, and properly compute the log of density evaluation.
Is the latter approach possible? Any thoughts about them?