def _calc_loglikelihood(self, model=None, tile=None):
        """Allows for fast local updates of log-likelihood"""
        if model is None:
            res = self.residuals
        else:
            res = model - self._data[tile.slicer]

        sig, isig = self.sigma, 1.0/self.sigma
        nlogs = -np.log(np.sqrt(2*np.pi)*sig)*res.size
        return -0.5*isig*isig*np.dot(res.flat, res.flat) + nlogs
def _calc_loglikelihood(self, model=None, tile=None):
        """Allows for fast local updates of log-likelihood"""
        if model is None:
            res = self.residuals
        else:
            res = model - self._data[tile.slicer]

        sig, isig = self.sigma, 1.0/self.sigma
        nlogs = -np.log(np.sqrt(2*np.pi)*sig)*res.size
        return -0.5*isig*isig*np.dot(res.flat, res.flat) + nlogs
def _calc_loglikelihood ( self , model = None , tile = None ) : if model is None : res = self . residuals else : res = model - self . _data [ tile . slicer ] sig , isig = self . sigma , 1.0 / self . sigma nlogs = - np . log ( np . sqrt ( 2 * np . pi ) * sig ) * res . size return - 0.5 * isig * isig * np . dot ( res . flat , res . flat ) + nlogs
Allows for fast local updates of log-likelihood
Allows for fast local updates of log - likelihood