Bagging (i.e., bootstrap aggregating) involves combining an ensemble of bootstrap estimators. We consider bagging for inference from noisy or incomplete measurements on a collection of interacting stochastic dynamic systems. Each system is called a unit, and each unit is associated with a spatial location. A motivating example arises in epidemiology, where each unit is a city: the majority of transmission occurs within a city, with smaller yet epidemiologically important interactions arising from disease transmission between cities. Monte~Carlo filtering methods used for inference on nonlinear non-Gaussian systems can suffer from a curse of dimensionality as the number of units increases. We introduce bagged filter (BF) methodology which combines an ensemble of Monte Carlo filters, using spatiotemporally localized weights to select successful filters at each unit and time. We obtain conditions under which likelihood evaluation using a BF algorithm can beat a curse of dimensionality, and we demonstrate applicability even when these conditions do not hold. BF can out-perform an ensemble Kalman filter on a coupled population dynamics model describing infectious disease transmission. A block particle filter also performs well on this task, though the bagged filter respects smoothness and conservation laws that a block particle filter can violate.

翻译：拖动( 套装) ( 套装( 套装 套装 套装 ) 包括 一组 靴子 测距器 。 我们考虑从对交互式随机动态系统的收集进行吵杂或不完整的测量中推断出 。 每个系统都称为一个单元, 每个单元都与空间位置相关。 流行病学中出现一个积极的例子, 每个单位都是城市: 大部分传播发生在城市内, 城市之间疾病传播的相互作用是小于但具有流行病学重要性的相互作用。 Monte~ Carlo 过滤非线性系统用于推断的方法随着单位数量的增加, 可能会受到维度的诅咒。 我们引入包装过滤( BF) 方法, 将蒙特卡洛过滤器的组合组合组合组合起来, 并且每个单元和时间都使用局部的重量来选择成功的过滤器。 我们得到的条件是, 使用 BF 算法评估的可能性可以击败维度的诅咒, 我们证明即使在这些条件不坚固的情况下, 我们也可以在非伽西系统上 的维度 。