def log_posterior(self, indices=None, ks=None):
        if ks is None:
            ks = np.arange(self.K)

        lp = self.log_likelihood(indices, ks)
        lp += self.log_prior(ks)
        return lp
dataset/ETHPy150Open slinderman/pyhawkes/pyhawkes/models.py/DiscreteTimeStandardHawkesModel.log_posterior/original