EnLLVM: Ensemble Based Nonlinear Bayesian Filtering Using Linear Latent Variable Models
This work addresses computational bottlenecks in filtering for high-dimensional dynamical systems, though it appears incremental as it builds on existing ensemble methods.
The authors tackled the challenge of real-time nonlinear Bayesian filtering in high-dimensional systems with intractable likelihoods by proposing an ensemble-based method using linear latent projections, achieving performance comparable to the ensemble Kalman filter on a Lorenz system.
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.