@cache<EOL><INDENT>def null_lml(self):<DEDENT>
np = self._nsamples * self._ntraits<EOL>scale = self.null_scale<EOL>return self._static_lml() / <NUM_LIT:2> - np * safe_log(scale) / <NUM_LIT:2> - np / <NUM_LIT:2><EOL>
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.