def logjacobian(self, **params):
        r"""Returns the log of the jacobian needed to transform pdfs in the
        ``variable_params`` parameter space to the ``sampling_params``
        parameter space.

        Let :math:`\mathbf{x}` be the set of variable parameters,
        :math:`\mathbf{y} = f(\mathbf{x})` the set of sampling parameters, and
        :math:`p_x(\mathbf{x})` a probability density function defined over
        :math:`\mathbf{x}`.
        The corresponding pdf in :math:`\mathbf{y}` is then:

        .. math::

            p_y(\mathbf{y}) =
                p_x(\mathbf{x})\left|\mathrm{det}\,\mathbf{J}_{ij}\right|,

        where :math:`\mathbf{J}_{ij}` is the Jacobian of the inverse transform
        :math:`\mathbf{x} = g(\mathbf{y})`. This has elements:

        .. math::

            \mathbf{J}_{ij} = \frac{\partial g_i}{\partial{y_j}}

        This function returns
        :math:`\log \left|\mathrm{det}\,\mathbf{J}_{ij}\right|`.


        Parameters
        ----------
        \**params :
            The keyword arguments should specify values for all of the variable
            args and all of the sampling args.

        Returns
        -------
        float :
            The value of the jacobian.
        """
        return numpy.log(abs(transforms.compute_jacobian(
            params, self.sampling_transforms, inverse=True)))
def logjacobian ( self , ** params ) : return numpy . log ( abs ( transforms . compute_jacobian ( params , self . sampling_transforms , inverse = True ) ) )
r"""Returns the log of the jacobian needed to transform pdfs in the
        ``variable_params`` parameter space to the ``sampling_params``
        parameter space.

        Let :math:`\mathbf{x}` be the set of variable parameters,
        :math:`\mathbf{y} = f(\mathbf{x})` the set of sampling parameters, and
        :math:`p_x(\mathbf{x})` a probability density function defined over
        :math:`\mathbf{x}`.
        The corresponding pdf in :math:`\mathbf{y}` is then:

        .. math::

            p_y(\mathbf{y}) =
                p_x(\mathbf{x})\left|\mathrm{det}\,\mathbf{J}_{ij}\right|,

        where :math:`\mathbf{J}_{ij}` is the Jacobian of the inverse transform
        :math:`\mathbf{x} = g(\mathbf{y})`. This has elements:

        .. math::

            \mathbf{J}_{ij} = \frac{\partial g_i}{\partial{y_j}}

        This function returns
        :math:`\log \left|\mathrm{det}\,\mathbf{J}_{ij}\right|`.


        Parameters
        ----------
        \**params :
            The keyword arguments should specify values for all of the variable
            args and all of the sampling args.

        Returns
        -------
        float :
            The value of the jacobian.