This paper introduces factored conditional filters, new filtering algorithms for simultaneously tracking states and estimating parameters in high-dimensional state spaces. The conditional nature of the algorithms is used to estimate parameters and the factored nature is used to decompose the state space into low-dimensional subspaces in such a way that filtering on these subspaces gives distributions whose product is a good approximation to the distribution on the entire state space. The conditions for successful application of the algorithms are that observations be available at the subspace level and that the transition model can be factored into local transition models that are approximately confined to the subspaces; these conditions are widely satisfied in computer science, engineering, and geophysical filtering applications. We give experimental results on tracking epidemics and estimating parameters in large contact networks that show the effectiveness of our approach.

翻译：本文提出了分解条件滤波算法，这是一种用于在高维状态空间中同时跟踪状态和估计参数的新型滤波算法。该算法的条件特性用于参数估计，而分解特性则用于将状态空间分解为低维子空间，使得在这些子空间上的滤波所得分布之积能良好逼近整个状态空间的分布。算法成功应用的条件包括：观测需在子空间层面可得，且转移模型可分解为近似局限于各子空间的局部转移模型；这些条件在计算机科学、工程学及地球物理滤波应用中普遍满足。我们通过在大型接触网络中跟踪流行病传播及估计参数的实验结果，验证了本方法的有效性。