def loglikelihood(self):
        """
        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]`
        """
        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
def loglikelihood(self):
        """
        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]`
        """
        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
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
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]`
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 ]