A decomposed multi-fidelity covariance formulation allows Vecchia approximation on latent processes and GLS mean removal to deliver scalable, fully likelihood-based fusion of noisy low-fidelity and accurate high-fidelity spatio-temporal data.
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences , volume=
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ARCH is a hierarchical flow-based generative model that enables tractable conditional intensity computation and arbitrary conditioning for spatiotemporal event distributions.
SNMPP builds a product-form neural influence kernel from a signed interaction network over event classes and a delay-aware monotonic temporal network to enable explicit discovery of inter-event relationships alongside strong prediction.
citing papers explorer
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A new framework for non-stationary spatio-temporal data fusion of multi-fidelity models
A decomposed multi-fidelity covariance formulation allows Vecchia approximation on latent processes and GLS mean removal to deliver scalable, fully likelihood-based fusion of noisy low-fidelity and accurate high-fidelity spatio-temporal data.
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Arbitrarily Conditioned Hierarchical Flows for Spatiotemporal Events
ARCH is a hierarchical flow-based generative model that enables tractable conditional intensity computation and arbitrary conditioning for spatiotemporal event distributions.
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Structured Neural Marked Point Processes for Interpretable Event Interaction Modeling
SNMPP builds a product-form neural influence kernel from a signed interaction network over event classes and a delay-aware monotonic temporal network to enable explicit discovery of inter-event relationships alongside strong prediction.