def null_lml(self):
        """
        Log of the marginal likelihood for the null hypothesis.

        It is implemented as ::

            2·log(p(Y)) = -n·log(2𝜋s) - log｜D｜ - n,

        Returns
        -------
        lml : float
            Log of the marginal likelihood.
        """
        n = self._nsamples
        scale = self.null_scale
        return (self._static_lml() - n * log(scale)) / 2
def null_lml(self):
        """
        Log of the marginal likelihood for the null hypothesis.

        It is implemented as ::

            2·log(p(Y)) = -n·log(2𝜋s) - log｜D｜ - n,

        Returns
        -------
        lml : float
            Log of the marginal likelihood.
        """
        n = self._nsamples
        scale = self.null_scale
        return (self._static_lml() - n * log(scale)) / 2
def null_lml ( self ) : n = self . _nsamples scale = self . null_scale return ( self . _static_lml ( ) - n * log ( scale ) ) / 2
Log of the marginal likelihood for the null hypothesis.

        It is implemented as ::

            2·log(p(Y)) = -n·log(2𝜋s) - log｜D｜ - n,

        Returns
        -------
        lml : float
            Log of the marginal likelihood.
Log of the marginal likelihood for the null hypothesis .