def _lml_optimal_scale(self):
        """
        Log of the marginal likelihood for optimal scale.

        Implementation for unrestricted LML::

        Returns
        -------
        lml : float
            Log of the marginal likelihood.
        """
        assert self._optimal["scale"]

        n = len(self._y)
        lml = -self._df * log2pi - self._df - n * log(self.scale)
        lml -= sum(npsum(log(D)) for D in self._D)
        return lml / 2
def _lml_optimal_scale(self):
        """
        Log of the marginal likelihood for optimal scale.

        Implementation for unrestricted LML::

        Returns
        -------
        lml : float
            Log of the marginal likelihood.
        """
        assert self._optimal["scale"]

        n = len(self._y)
        lml = -self._df * log2pi - self._df - n * log(self.scale)
        lml -= sum(npsum(log(D)) for D in self._D)
        return lml / 2
def _lml_optimal_scale ( self ) : assert self . _optimal [ "scale" ] n = len ( self . _y ) lml = - self . _df * log2pi - self . _df - n * log ( self . scale ) lml -= sum ( npsum ( log ( D ) ) for D in self . _D ) return lml / 2
Log of the marginal likelihood for optimal scale.

        Implementation for unrestricted LML::

        Returns
        -------
        lml : float
            Log of the marginal likelihood.
Log of the marginal likelihood for optimal scale .