(self, theta)
return (1 / (self.sigma * np.sqrt(2 * np.pi))) *\
               (- theta / (self.sigma ** 2) * np.exp(- (theta ** 2) / (2 * self.sigma ** 2)))
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.
        """
        return (1 / (self.sigma * np.sqrt(2 * np.pi))) *\
               (- theta / (self.sigma ** 2) * np.exp(- (theta ** 2) / (2 * self.sigma ** 2)))
def gradient ( self , theta ) : return ( 1 / ( self . sigma * np . sqrt ( 2 * np . pi ) ) ) * ( - theta / ( self . sigma ** 2 ) * np . exp ( - ( theta ** 2 ) / ( 2 * self . sigma ** 2 ) ) )