Many real-world problems require one to estimate parameters of interest, in a Bayesian framework, from data that are collected sequentially in time. Conventional methods for sampling from posterior distributions, such as {Markov Chain Monte Carlo} can not efficiently address such problems as they do not take advantage of the data's sequential structure. To this end, sequential methods which seek to update the posterior distribution whenever a new collection of data become available are often used to solve these types of problems. Two popular choices of sequential method are the Ensemble Kalman filter (EnKF) and the sequential Monte Carlo sampler (SMCS). While EnKF only computes a Gaussian approximation of the posterior distribution, SMCS can draw samples directly from the posterior. Its performance, however, depends critically upon the kernels that are used. In this work, we present a method that constructs the kernels of SMCS using an EnKF formulation, and we demonstrate the performance of the method with numerical examples.

翻译：许多现实世界问题要求人们从巴耶斯框架、从时间顺序收集的数据中估算出感兴趣的参数。从后面分布的常规采样方法,例如{马科夫链-蒙特卡洛},无法有效地解决没有利用数据顺序结构的问题。为此,经常使用连续方法解决这类类型的问题。两种流行的顺序方法是Ensemble Kalman过滤器(EnKF)和随后的Monte Carlo采样器(SMCS)。虽然EnKF只计算远地点分布的高山近似值,但SMCS只能直接从后面的分布中提取样品。然而,其性能主要取决于使用的内核。在这项工作中,我们提出了一个方法,用EnKF的配方构建SMCS的内核,我们用数字实例来展示该方法的性能。