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, ___ = self._model(beta)
        loc = np.ones(lmda.shape[0])*self.latent_variables.z_list[-2].prior.transform(beta[-2]) + self.latent_variables.z_list[-1].prior.transform(beta[-1])*np.exp(lmda/2.0)
        return -np.sum(ss.t.logpdf(x=Y,
            df=self.latent_variables.z_list[-3].prior.transform(beta[-3]),
            loc=loc,scale=np.exp(lmda/2.0)))
def neg_loglik ( self , beta ) : lmda , Y , ___ = self . _model ( beta ) loc = np . ones ( lmda . shape [ 0 ] ) * self . latent_variables . z_list [ - 2 ] . prior . transform ( beta [ - 2 ] ) + self . latent_variables . z_list [ - 1 ] . prior . transform ( beta [ - 1 ] ) * np . exp ( lmda / 2.0 ) return - np . sum ( ss . t . logpdf ( x = Y , df = self . latent_variables . z_list [ - 3 ] . prior . transform ( beta [ - 3 ] ) , loc = loc , scale = np . exp ( lmda / 2.0 ) ) )
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