I’d like to ask a question about how to define a posterior inferred in model_1 as an input random variable in model_2.
This might be pretty simple, but I don’t know how.
For example, a parameter “a” is estimated in model_1.
Then, I’d like to use the posterior “a” estimated from model_1 as the input random variable in model_2, where another parameters b and c are estimated (“a” is not estimated but it is an input of model_2).
In this case, how can I define posterior “a” as a random variable in model_2?
Should I conduct a kernel density estimation that approximates the posterior of “a” and then define it in model_2 using a built-in distribution in PyMC3?
Or is there a direct method that enables users to use inferred posteriors as a prior in another model without approximating a posterior to a built-in distribution in PyMC3?
Thank you in advance.