e4bdbe76308dbeff50741415ab8c080ef99a7d70,pymc3/distributions/multivariate.py,LKJCholeskyCov,logp,#LKJCholeskyCov#Any#,698

Before Change

        logp_sd = self.sd_dist.logp(sd_vals).sum()

        corr_diag = x[diag_idxs] / sd_vals
        corr_logdet = np.log(corr_diag).sum()

        // Compute the log det jacobian of the second transformation
        // described in the docstring.
        count = np.arange(self.n - 1)
        det_invjac = - (count * tt.log(sd_vals[1:])).sum()
        det_invjac += - tt.log(x[diag_idxs]).sum() + tt.log(x[0])

        norm = _lkj_normalizing_constant(eta, self.n)

        return norm + (self.n - 1) * corr_logdet + logp_sd + det_invjac


class LKJCorr(Continuous):

After Change

        // Compute the log det jacobian of the second transformation
        // described in the docstring.
        idx = tt.arange(n)
        det_invjac = tt.log(corr_diag) - idx * tt.log(sd_vals)
        det_invjac = det_invjac.sum()

        norm = _lkj_normalizing_constant(eta, self.n)

        return norm + logp_lkj + logp_sd + det_invjac

Instances