2017/08/08 by Xiao Lin, Lin, Xiao, Gabriel Terejanu +1
Computer Science · Mathematics · #Algorithm #Artificial intelligence #Bayesian probability #Computation (stat.CO) #Computer science #Covariance #Ensemble Kalman filter #Extended Kalman filter #FOS: Computer and information sciences #Gaussian #Gaussian Processes and Bayesian Inference #Kalman filter #Latent variable #Machine Learning (cs.LG) #Mathematics #Nonlinear system #Particle filter #Statistics #Target Tracking and Data Fusion in Sensor Networks #Time Series Analysis and Forecasting #cs.LG #stat.CO
paper · pdf · doi:10.48550/arxiv.1708.02340
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2017/08/08 · arxiv created 2019/05/31 · arxiv updated 2019/06/05 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28
Real-time nonlinear Bayesian filtering algorithms are overwhelmed by data volume, velocity and increasing complexity of computational models. In this paper, we propose a novel ensemble based nonlinear Bayesian filtering approach which only requires a small number of simulations and can be applied to high-dimensional systems in the presence of intractable likelihood functions. The proposed approach uses linear latent projections to estimate the joint probability distribution between states, parameters, and observables using a mixture of Gaussian components generated by the reconstruction error for each ensemble member. Since it leverages the computational machinery behind linear latent variable models, it can achieve fast implementations without the need to compute high-dimensional sample covariance matrices. The performance of the proposed approach is compared with the performance of ensemble Kalman filter on a high-dimensional Lorenz nonlinear dynamical system.