pymc.Model.logp_dlogp_function#

Model.logp_dlogp_function(grad_vars=None, tempered=False, **kwargs)[source]#

Compile an Aesara function that computes logp and gradient.

Parameters
grad_vars: list of random variables, optional

Compute the gradient with respect to those variables. If None, use all free random variables of this model.

tempered: bool

Compute the tempered logp free_logp + alpha * observed_logp. alpha can be changed using ValueGradFunction.set_weights([alpha]).