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

        It is implemented as ::

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

        for which s and 𝚩 are optimal.

        Returns
        -------
        lml : float
            Log of the marginal likelihood.
        """
        np = self._nsamples * self._ntraits
        scale = self.null_scale
        return self._static_lml() / 2 - np * safe_log(scale) / 2 - np / 2
Log of the marginal likelihood for the null hypothesis.

        It is implemented as ::

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

        for which s and 𝚩 are optimal.

        Returns
        -------
        lml : float
            Log of the marginal likelihood.