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:
            if self.a[hyper_deriv] == 1.0 and theta[hyper_deriv] == 0.0:
                return -self.b[hyper_deriv]
            else:
                return (self.a[hyper_deriv] - 1.0) / theta[hyper_deriv] - self.b[hyper_deriv]
        ll = 0
        for v, a, b in zip(theta, self.a, self.b):
            ll += scipy.stats.gamma.logpdf(v, a, loc=0, scale=1.0 / b)
        return ll
def __call__ ( self , theta , hyper_deriv = None ) : if hyper_deriv is not None : if self . a [ hyper_deriv ] == 1.0 and theta [ hyper_deriv ] == 0.0 : return - self . b [ hyper_deriv ] else : return ( self . a [ hyper_deriv ] - 1.0 ) / theta [ hyper_deriv ] - self . b [ hyper_deriv ] ll = 0 for v , a , b in zip ( theta , self . a , self . b ) : ll += scipy . stats . gamma . logpdf ( v , a , loc = 0 , scale = 1.0 / b ) 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.