r"""
        Gaussian log likelihood.

        Parameters
        ----------
        y: ndarray
            array of 0, 1 valued integers of targets
        f: ndarray
            latent function from the GLM prior (:math:`\mathbf{f} =
            \boldsymbol\Phi \mathbf{w}`)
        var: float, ndarray, optional
            The variance of the distribution, if not input, the initial value
            of variance is used.

        Returns
        -------
        logp: ndarray
            the log likelihood of each y given each f under this
            likelihood.

def loglike(self, y, f, var=None):
        r"""
        Gaussian log likelihood.

        Parameters
        ----------
        y: ndarray
            array of 0, 1 valued integers of targets
        f: ndarray
            latent function from the GLM prior (:math:`\mathbf{f} =
            \boldsymbol\Phi \mathbf{w}`)
        var: float, ndarray, optional
            The variance of the distribution, if not input, the initial value
            of variance is used.

        Returns
        -------
        logp: ndarray
            the log likelihood of each y given each f under this
            likelihood.
        """
        # way faster than calling norm.logpdf
        var = self._check_param(var)
        y, f = np.broadcast_arrays(y, f)
        ll = - 0.5 * (np.log(2 * np.pi * var) + (y - f)**2 / var)
        return ll