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Parameter learning for high-dimensional, partially observed, and nonlinearstochastic processes is a methodological challenge. Spatiotemporal diseasetransmission systems provide examples of such processes giving rise to openinference problems. We propose the iterated block particle filter (IBPF)algorithm for learning high-dimensional parameters over graphical state spacemodels with general state spaces, measures, transition densities and graphstructure. Theoretical performance guarantees are obtained on beating the curseof dimensionality (COD), algorithm convergence, and likelihood maximization.Experiments on a highly nonlinear and non-Gaussian spatiotemporal model formeasles transmission reveal that the iterated ensemble Kalman filter algorithm(Li et al. (2020)) is ineffective and the iterated filtering algorithm (Ionideset al. (2015)) suffers from the COD, while our IBPF algorithm beats CODconsistently across various experiments with different metrics.

Iterated Block Particle Filter for High-dimensional Parameter Learning: Beating the Curse of Dimensionality