mimc.py 文件源码

def _estimateAll(self):
        self._estimateQParams()
        self.iters[-1].Vl_estimate = self.fn.Norm(self.all_itr.calcDeltaVl()) \
                                     if not self.params.bayesian \
                                        else self._estimateBayesianVl()
        self.iters[-1].Wl_estimate = self.fn.WorkModel(lvls=self.last_itr.get_lvls())
        self.iters[-1].bias = self._estimateBias()
        Ca = norm.ppf(self.params.confidence)
        self.iters[-1].stat_error = np.inf if np.any(self.last_itr.M == 0) \
                                    else Ca * \
                                         np.sqrt(np.sum(self.Vl_estimate / self.last_itr.M))