def neg_loglik(self, beta):
        """ Creates the negative log-likelihood of the model

        Parameters
        ----------
        beta : np.array
            Contains untransformed starting values for latent variables

        Returns
        ----------
        The negative logliklihood of the model
        """     

        lmda, Y, ___, theta = self._model(beta)
        return -np.sum(logpdf(Y, self.latent_variables.z_list[-3].prior.transform(beta[-3]), 
            loc=theta, scale=np.exp(lmda/2.0), 
            skewness = self.latent_variables.z_list[-4].prior.transform(beta[-4])))
def neg_loglik ( self , beta ) : lmda , Y , ___ , theta = self . _model ( beta ) return - np . sum ( logpdf ( Y , self . latent_variables . z_list [ - 3 ] . prior . transform ( beta [ - 3 ] ) , loc = theta , scale = np . exp ( lmda / 2.0 ) , skewness = self . latent_variables . z_list [ - 4 ] . prior . transform ( beta [ - 4 ] ) ) )
Creates the negative log-likelihood of the model

        Parameters
        ----------
        beta : np.array
            Contains untransformed starting values for latent variables

        Returns
        ----------
        The negative logliklihood of the model