March 13, 2024, 4:42 a.m. | Feras Saad, Jacob Burnim, Colin Carroll, Brian Patton, Urs K\"oster, Rif A. Saurous, Matthew Hoffman

cs.LG updates on

arXiv:2403.07657v1 Announce Type: new
Abstract: Spatiotemporal datasets, which consist of spatially-referenced time series, are ubiquitous in many scientific and business-intelligence applications, such as air pollution monitoring, disease tracking, and cloud-demand forecasting. As modern datasets continue to increase in size and complexity, there is a growing need for new statistical methods that are flexible enough to capture complex spatiotemporal dynamics and scalable enough to handle large prediction problems. This work presents the Bayesian Neural Field (BayesNF), a domain-general statistical model for …

arxiv bayesian cs.lg fields prediction scalable stat.ap type

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