Model with multiple likelihoods - how do they contribute to overall log-likelihood during sampling?

[jonsedar]

Thanks @ricardoV94, told you it was stupid question

Where I was hoping to lead to is around how the pointwise log-likelihood is stored, and now I see that they’re simply stored with increasing dimensions as needed

e.g.

with pm.Model() as m1:
  mu = pm.Normal("mu", shape=(2,))
  y = pm.Normal("y", mu=mu, observed=[0, 1])
  idata_m1 = pm.sample(idata_kwargs={"log_likelihood": True})

idata_m1.log_likelihood

with pm.Model() as m2:
  mu = pm.Normal("mu", shape=(2,))
  y1 = pm.Normal("y1", mu=mu[0], observed=[0])
  y2 = pm.Normal("y2", mu=mu[1], observed=[1])

  idata_m2 = pm.sample(idata_kwargs={"log_likelihood": True})

idata_m2.log_likelihood