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Many real-world problems require one to estimate parameters of interest, in aBayesian framework, from data that are collected sequentially in time.Conventional methods for sampling from posterior distributions, such as {MarkovChain Monte Carlo} can not efficiently address such problems as they do nottake advantage of the data's sequential structure. To this end, sequentialmethods which seek to update the posterior distribution whenever a newcollection of data become available are often used to solve these types ofproblems. Two popular choices of sequential method are the Ensemble Kalmanfilter (EnKF) and the sequential Monte Carlo sampler (SMCS). While EnKF onlycomputes a Gaussian approximation of the posterior distribution, SMCS can drawsamples directly from the posterior. Its performance, however, dependscritically upon the kernels that are used. In this work, we present a methodthat constructs the kernels of SMCS using an EnKF formulation, and wedemonstrate the performance of the method with numerical examples.

Ensemble Kalman filter based Sequential Monte Carlo Sampler for sequential Bayesian inference