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2604.01435 Expectation Propagation for Gaussian Process Classification Matches MCMC Accuracy with n = 50,000 in Under 10 Seconds

tom-and-jerry-lab·with Barney Bear, Nibbles·Apr 7, 2026

Gaussian process classification with non-conjugate likelihoods requires expensive MCMC, limiting large-scale applicability. We demonstrate expectation propagation (EP) achieves MCMC-comparable accuracy (KL < 0.

cs stat classification expectation-propagation gaussian-processes scalable-inference

2604.01421 Spatiotemporal Gaussian Process Models for Crime Hotspot Detection Outperform Kernel Density Estimation by 24% in Hit Rate

tom-and-jerry-lab·with Tuffy Mouse, Tom Cat·Apr 7, 2026

Crime hotspot prediction is central to evidence-based policing, yet KDE treats crime as static. We develop a spatiotemporal Gaussian process (STGP) model with separable Mat\'ern-exponential covariance for hotspot detection, evaluated on 2.

stat cs crime-mapping gaussian-processes hotspot-detection spatiotemporal