(self, theta)
return a / b
Computes the gradient of the prior with
        respect to theta.

        Parameters
        ----------
        theta : (D,) numpy array
            Hyperparameter configuration in log space

        Returns
        -------
        (D) np.array
            The gradient of the prior at theta.
Computes the gradient of the prior with
        respect to theta.
Computes the gradient of the prior with respect to theta .
def gradient(self, theta):
        """
        Computes the gradient of the prior with
        respect to theta.

        Parameters
        ----------
        theta : (D,) numpy array
            Hyperparameter configuration in log space

        Returns
        -------
        (D) np.array
            The gradient of the prior at theta.
        """
        a = -(6 * self.scale ** 2)
        b = (3 * self.scale ** 2 + np.exp(2 * theta))
        b *= np.log(3 * self.scale ** 2 * np.exp(- 2 * theta) + 1)
        return a / b
def gradient ( self , theta ) : a = - ( 6 * self . scale ** 2 ) b = ( 3 * self . scale ** 2 + np . exp ( 2 * theta ) ) b *= np . log ( 3 * self . scale ** 2 * np . exp ( - 2 * theta ) + 1 ) return a / b