pymc.model.core.Model.compile_dlogp

Model.compile_dlogp(vars=None, jacobian=True, **compile_kwargs)

Compiled gradient of the joint log-probability density of the model.

Parameters:

varsVariable, sequence of Variable or None, default None

Compute the gradient with respect to values of these variables. If None, use all continuous free (unobserved) variables defined in the model.

jacobianbool, default True

If True, add Jacobian contributions associated with automatic variable transformations, so that the result is the true density of transformed random variables. See pymc.distributions.transforms for details.

**compile_kwargsdict

Extra arguments passed to self.compile_fn().

Returns:

PointFunc

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

See also

dlogp()

gradient of log-probability density as a Variable (in a symbolic form).

compile_logp()

log-probability density as a compiled function.

compile_d2logp()

Hessian of log-probability density as a compiled function.