def logpdf_link(self, link_f, y, Y_metadata=None):
ln_det_cov = np.log(self.variance)<EOL>return -(<NUM_LIT:1.0>/(<NUM_LIT:2>*self.variance))*((y-link_f)**<NUM_LIT:2>) - <NUM_LIT:0.5>*ln_det_cov - <NUM_LIT:0.5>*np.log(<NUM_LIT>*np.pi)<EOL>
Log likelihood function given link(f)

.. math::
    \\ln p(y_{i}|\\lambda(f_{i})) = -\\frac{N \\ln 2\\pi}{2} - \\frac{\\ln |K|}{2} - \\frac{(y_{i} - \\lambda(f_{i}))^{T}\\sigma^{-2}(y_{i} - \\lambda(f_{i}))}{2}

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