pymc.model.core.Model.d2logp

Model.d2logp(vars=None, jacobian=True)

Hessian of the models log-probability w.r.t. vars.

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.

Returns:

d²logp graph