BayesianBasisExpansionTimeSeries.logp_dlogp_function#

BayesianBasisExpansionTimeSeries.logp_dlogp_function(grad_vars=None, tempered=False, initial_point=None, ravel_inputs=None, **kwargs)#

Compile a PyTensor 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]).

  • initial_point (dict[str, ndarray] | None)

  • ravel_inputs (bool | None)