1 citations · 1 across the 4 of their papers we have counts for
4 papers
Parameter estimation from aggregate observations: A Wasserstein distance based sequential Monte Carlo sampler
Chen Cheng, Linjie Wen, Jinglai Li
In this work we study systems consisting of a group of moving particles. In such systems, often some important parameters are unknown and have to be estimated from observed data. S…
Affine-Mapping based Variational Ensemble Kalman Filter
Linjie Wen, Jinglai Li
We propose an affine-mapping based variational Ensemble Kalman filter for sequential Bayesian filtering problems with generic observation models. Specifically, the proposed method…
Ensemble Kalman filter based Sequential Monte Carlo Sampler for sequential Bayesian inference
Jiangqi Wu, Linjie Wen, Peter L Green +2
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 sampli…
A defensive marginal particle filtering method for data assimilation
Linjie Wen, Jiangqi Wu, Linjun Lu +1
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 sp…