def d3logpdf_df3(self, f, y, Y_metadata=None):
        """
        Evaluates the link function link(f) then computes the third derivative of log likelihood using it
        Uses the Faa di Bruno's formula for the chain rule

        .. math::
            \\frac{d^{3}\\log p(y|\\lambda(f))}{df^{3}} = \\frac{d^{3}\\log p(y|\\lambda(f)}{d\\lambda(f)^{3}}\\left(\\frac{d\\lambda(f)}{df}\\right)^{3} + 3\\frac{d^{2}\\log p(y|\\lambda(f)}{d\\lambda(f)^{2}}\\frac{d\\lambda(f)}{df}\\frac{d^{2}\\lambda(f)}{df^{2}} + \\frac{d\\log p(y|\\lambda(f)}{d\\lambda(f)}\\frac{d^{3}\\lambda(f)}{df^{3}}

        :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: third derivative of log likelihood evaluated for this point
        :rtype: float
        """
        if isinstance(self.gp_link, link_functions.Identity):
            d3logpdf_df3 = self.d3logpdf_dlink3(f, y, Y_metadata=Y_metadata)
        else:
            inv_link_f = self.gp_link.transf(f)
            d3logpdf_dlink3 = self.d3logpdf_dlink3(inv_link_f, y, Y_metadata=Y_metadata)
            dlink_df = self.gp_link.dtransf_df(f)
            d2logpdf_dlink2 = self.d2logpdf_dlink2(inv_link_f, y, Y_metadata=Y_metadata)
            d2link_df2 = self.gp_link.d2transf_df2(f)
            dlogpdf_dlink = self.dlogpdf_dlink(inv_link_f, y, Y_metadata=Y_metadata)
            d3link_df3 = self.gp_link.d3transf_df3(f)
            d3logpdf_df3 = chain_3(d3logpdf_dlink3, dlink_df, d2logpdf_dlink2, d2link_df2, dlogpdf_dlink, d3link_df3)
        return d3logpdf_df3
Evaluates the link function link(f) then computes the third derivative of log likelihood using it
        Uses the Faa di Bruno's formula for the chain rule

        .. math::
            \\frac{d^{3}\\log p(y|\\lambda(f))}{df^{3}} = \\frac{d^{3}\\log p(y|\\lambda(f)}{d\\lambda(f)^{3}}\\left(\\frac{d\\lambda(f)}{df}\\right)^{3} + 3\\frac{d^{2}\\log p(y|\\lambda(f)}{d\\lambda(f)^{2}}\\frac{d\\lambda(f)}{df}\\frac{d^{2}\\lambda(f)}{df^{2}} + \\frac{d\\log p(y|\\lambda(f)}{d\\lambda(f)}\\frac{d^{3}\\lambda(f)}{df^{3}}

        :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: third derivative of log likelihood evaluated for this point
        :rtype: float