Likelihood function given link(f)

        .. math::
            p(y_{i}|\\lambda(f_{i})) = \\frac{\\lambda(f_{i})^{y_{i}}}{y_{i}!}e^{-\\lambda(f_{i})}

        :param link_f: latent variables link(f)
        :type link_f: Nx1 array
        :param y: data
        :type y: Nx1 array
        :param Y_metadata: Y_metadata which is not used in poisson distribution
        :returns: likelihood evaluated for this point
        :rtype: float

def pdf_link(self, link_f, y, Y_metadata=None):
        """
        Likelihood function given link(f)

        .. math::
            p(y_{i}|\\lambda(f_{i})) = \\frac{\\lambda(f_{i})^{y_{i}}}{y_{i}!}e^{-\\lambda(f_{i})}

        :param link_f: latent variables link(f)
        :type link_f: Nx1 array
        :param y: data
        :type y: Nx1 array
        :param Y_metadata: Y_metadata which is not used in poisson distribution
        :returns: likelihood evaluated for this point
        :rtype: float
        """
        assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape
        return np.exp(self.logpdf_link(link_f, y, Y_metadata))