def dlogpdf_df(self, f, y, Y_metadata=None):
if isinstance(self.gp_link, link_functions.Identity):<EOL><INDENT>return self.dlogpdf_dlink(f, y, Y_metadata=Y_metadata)<EOL><DEDENT>else:<EOL><INDENT>inv_link_f = self.gp_link.transf(f)<EOL>dlogpdf_dlink = self.dlogpdf_dlink(inv_link_f, y, Y_metadata=Y_metadata)<EOL>dlink_df = self.gp_link.dtransf_df(f)<EOL>return chain_1(dlogpdf_dlink, dlink_df)<EOL><DEDENT>
Evaluates the link function link(f) then computes the derivative of log likelihood using it
Uses the Faa di Bruno's formula for the chain rule

.. math::
    \\frac{d\\log p(y|\\lambda(f))}{df} = \\frac{d\\log p(y|\\lambda(f))}{d\\lambda(f)}\\frac{d\\lambda(f)}{df}

:param f: latent variables f
:type f: Nx1 array
:param y: data
:type y: Nx1 array
:param Y_metadata: Y_metadata which is not used in student t distribution - not used
:returns: derivative of log likelihood evaluated for this point
:rtype: 1xN array