def pdf_link(self, link_f, y, Y_metadata=None):
assert np.atleast_1d(link_f).shape == np.atleast_1d(y).shape<EOL>return np.exp(self.logpdf_link(link_f, y, Y_metadata))<EOL>
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