pymc.model.core.Model.logp

Model.logp(vars=None, jacobian=True, sum=True)

Elemwise log-probability of the model.

Parameters:

vars: list of random variables or potential terms, optional

Compute the gradient with respect to those variables. If None, use all free and observed random variables, as well as potential terms in model.

jacobian:

Whether to include jacobian terms in logprob graph. Defaults to True.

sum:

Whether to sum all logp terms or return elemwise logp for each variable. Defaults to True.

Returns:

Logp graph(s)