论文标题
划分并征服顺序蒙特卡洛方法用于高维滤波
A divide and conquer sequential Monte Carlo approach to high dimensional filtering
论文作者
论文摘要
我们提出了一种分裂和诱导方法来过滤，将状态变量分解为低维组件，可以成功地应用标准的粒子过滤工具并递归合并它们以恢复完整的过滤分布。与竞争方法相比，它不太依赖于过渡密度和观察可能性的分解，并且可以应用于更广泛的模型。将性能与基准问题上的最新方法进行了比较，并证明了所提出的方法在适用这些方法的设置中可以广泛可比性，并且可以在无法使用的设置中应用。
We propose a divide-and-conquer approach to filtering which decomposes the state variable into low-dimensional components to which standard particle filtering tools can be successfully applied and recursively merges them to recover the full filtering distribution. It is less dependent upon factorization of transition densities and observation likelihoods than competing approaches and can be applied to a broader class of models. Performance is compared with state-of-the-art methods on a benchmark problem and it is demonstrated that the proposed method is broadly comparable in settings in which those methods are applicable, and that it can be applied in settings in which they cannot.
