def __call__(self, theta, hyper_deriv=None):
        """Evaluate the prior log-PDF at the given values of the hyperparameters, theta.
        
        Parameters
        ----------
        theta : array-like, (`num_params`,)
            The hyperparameters to evaluate the log-PDF at.
        """
        if hyper_deriv is not None:
            return -1.0 / theta[hyper_deriv] * (
                1.0 + scipy.log(theta[hyper_deriv] / self.emu[hyper_deriv]) /
                self.sigma[hyper_deriv]**2.0
            )
        ll = 0
        for v, s, em in zip(theta, self.sigma, self.emu):
            ll += scipy.stats.lognorm.logpdf(v, s, loc=0, scale=em)
        return ll
def __call__ ( self , theta , hyper_deriv = None ) : if hyper_deriv is not None : return - 1.0 / theta [ hyper_deriv ] * ( 1.0 + scipy . log ( theta [ hyper_deriv ] / self . emu [ hyper_deriv ] ) / self . sigma [ hyper_deriv ] ** 2.0 ) ll = 0 for v , s , em in zip ( theta , self . sigma , self . emu ) : ll += scipy . stats . lognorm . logpdf ( v , s , loc = 0 , scale = em ) return ll
Evaluate the prior log-PDF at the given values of the hyperparameters, theta.
        
        Parameters
        ----------
        theta : array-like, (`num_params`,)
            The hyperparameters to evaluate the log-PDF at.