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A Robust High-Dimensional Bayesian Filter: THE Stochastic GH-GEnKF

Ensemble Kalman filtering techniques are very popular recursive Monte Carlo algorithms, developed in the geophysics literature, for inference in high-dimensional spatio-temporal state-space models. Unfortunately, these techniques tend to be inefficient in the presence of heavy-tailed data such as outliers. In this paper, we propose a robust ensemble-based inference technique by introducing the generalized hyperbolic distribution as a flexible and general distribution for the observation errors. Simulation results empirically show that the proposed algorithm achieves significantly better performance compared to existing algorithms.