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

@article{Ning2021IteratedBP, title={Iterated Block Particle Filter for High-dimensional Parameter Learning: Beating the Curse of Dimensionality}, author={Ning Ning and Edward L. Ionides}, journal={ArXiv}, year={2021}, volume={abs/2110.10745} }

Parameter learning for high-dimensional, partially observed, and nonlinear stochastic processes is a methodological challenge. Spatiotemporal disease transmission systems provide examples of such processes giving rise to open inference problems. We propose the iterated block particle filter (IBPF) algorithm for learning high-dimensional parameters over graphical state space models with general state spaces, measures, transition densities and graph structure. Theoretical performance guarantees…

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