code: def logpdf_link(self, link_f, y, Y_metadata=None):
          
          ln_det_cov = np.log(self.variance)
          return -(1.0/(2*self.variance))*((y-link_f)**2) - 0.5*ln_det_cov - 0.5*np.log(2.*np.pi)
docs: 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