code: def null_lml(self):
          
          n = self._nsamples
          scale = self.null_scale
          return (self._static_lml() - n * log(scale)) / 2
docs: 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.