code: def loglikelihood(self):
          
          sig = self.hyper_parameters.get_values('sigma')
          err = self.error
          N = np.size(self.data)
          return -0.5*err/sig**2 - np.log(np.sqrt(2*np.pi)*sig)*N
docs: Class property: loglikelihood calculated by the model error,
          :math:`\\mathcal{L} = - \\frac{1}{2} \\sum\\left[
          \\left(\\frac{D_i - M_i(\\theta)}{\sigma}\\right)^2
          + \\log{(2\pi \sigma^2)} \\right]`