Jiangqi Wu | Searcharxiv

arXiv2022

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

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.

stat.ME↗

arXiv2019

A defensive marginal particle filtering method for data assimilation

Particle filtering (PF) is an often used method to estimate the states of dynamical systems. A major limitation of the standard PF method is that the dimensionality of the state space increases as the time proceeds and eventually may cause degeneracy of the algorithm. A possible approach to alleviate the degeneracy issue is to compute the marginal posterior distribution at each time step, which leads to the so-called marginal PF method. A key issue in the marginal PF method is to construct a good sampling distribution in the marginal space. When the posterior distribution is close to Gaussian, the Ensemble Kalman filter (EnKF) method can usually provide a good sampling distribution; however the EnKF approximation may fail completely when the posterior is strongly non-Gaussian. In this work we propose a defensive marginal PF (DMPF) algorithm which constructs a sampling distribution in the marginal space by combining the standard PF and the EnKF approximation using a multiple importance sampling (MIS) scheme. An important feature of the proposed algorithm is that it can automatically adjust the relative weight of the PF and the EnKF components in the MIS scheme in each step, according to how non-Gaussian the posterior is. With numerical examples we demonstrate that the proposed method can perform well regardless of whether the posteriors can be well approximated by Gaussian.

stat.CO↗