pymc.model.core.Model.d2logp

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

Hessian 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.

negate_outputbool, default True

If True, change the sign of the output and return the opposite of the Hessian.

Returns:

Variable

See also

compile_d2logp()

Hessian of log-probability density as a compiled function.

logp()

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

dlogp()

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