pymc.model.core.Model.compile_d2logp#

Model.compile_d2logp(vars=None, jacobian=True, negate_output=True, **compile_kwargs)[source]#

Compiled log probability density hessian function.

The function expects as input a dictionary with the same structure as self.initial_point()

Parameters:

varslist 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.
jacobianbool: Whether to include jacobian terms in logprob graph. Defaults to True.