def expected_log_W_given_A(self, A):
        """
        Compute the expected log W given A under the variational approximation
        """
        return A * (np.log(self.mf_v_1) - psi(self.mf_kappa_1)) + \
               (1.0 - A) * (psi(self.mf_kappa_0) - np.log(self.mf_v_0))
dataset/ETHPy150Open slinderman/pyhawkes/pyhawkes/internals/weights.py/GammaMixtureWeights.expected_log_W_given_A/swapped operands of `-`