code: def null_lml(self):
          
          np = self._nsamples * self._ntraits
          scale = self.null_scale
          return self._static_lml() / 2 - np * safe_log(scale) / 2 - np / 2
docs: 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.