Given a model defined in Turing I would like to calculate the joint probability function over all the variables. Say I have the model
@model function model(y) mu ~ Normal(0, 1) y ~ Normal(mu, 1) end y_obs = 1 mu = 1
With the prob macro I can do
prob"mu = mu, y = y_obs | model = model"
and it gives me exactly what I want. However, now I would like to be able to not have to explicitly pass in the value of
y_obs and instead do
m = model(y_obs) prob"mu = mu | model = m"
Is that somehow possible?
My end goal is to create a function which only takes as input an instantiated model and returns the joint probability function. So I want to have a function
get_joint_prob such that:
m = model(y_obs) joint_prob = get_joint_prob(m) joint_prob((mu=mu,)) == prob"mu = mu, y = y_obs | model = model"
joint_prob takes in a named tuple so that it can deal with multiple variables if necessary).
I have already read through the documentation on the Turing side and looked a bit at the implementation of the prob macro and the VarInfo stuff but I didn’t quite understand all of it well enough to get something to work. Any pointers would be really helpful!