def gamma_log_prob(self): loggamma = param_to_array(self.gamma).copy() loggamma[loggamma > -40] = -np.log1p(np.exp(-loggamma[loggamma > -40])) loggamma1 = -param_to_array(self.gamma).copy() loggamma1[loggamma1 > -40] = -np.log1p( np.exp(-loggamma1[loggamma1 > -40])) return loggamma, loggamma1
def gamma_probabilities(self): prob = np.zeros_like(param_to_array(self.gamma)) prob[self.gamma > -710] = 1. / (1. + np.exp(-self.gamma[self.gamma > -710])) prob1 = -np.zeros_like(param_to_array(self.gamma)) prob1[self.gamma < 710] = 1. / (1. + np.exp(self.gamma[self.gamma < 710])) return prob, prob1
def gamma_log_prob(self): loggamma = param_to_array(self.gamma).copy() loggamma[loggamma>-40] = -np.log1p(np.exp(-loggamma[loggamma>-40])) loggamma1 = -param_to_array(self.gamma).copy() loggamma1[loggamma1>-40] = -np.log1p(np.exp(-loggamma1[loggamma1>-40])) return loggamma,loggamma1
def gamma_probabilities(self): prob = np.zeros_like(param_to_array(self.gamma)) prob[self.gamma>-710] = 1./(1.+np.exp(-self.gamma[self.gamma>-710])) prob1 = -np.zeros_like(param_to_array(self.gamma)) prob1[self.gamma<710] = 1./(1.+np.exp(self.gamma[self.gamma<710])) return prob, prob1