The paper introduces a divide-and-conquer neural Bayes estimation framework for spatial and spatiotemporal point process models. It uses transformer-based architectures on partitions of the domain and an attention mechanism to combine local summaries, enabling direct inference from raw point pattern data. Simulation studies show reliable inference with better scalability than existing neural and classical likelihood-based methods, and the approach is applied to whale call data in Cape Cod Bay, producing parameter estimates and credible intervals at negligible cost after training.
arXiv:2606. 14313v1 Announce Type: cross Abstract: Real-world spatio-temporal forecasting must handle irregular time points, spatially sparse observations, and the need for uncertainty quantification.
arXiv:2603. 23746v2 Announce Type: replace Abstract: Events in spatiotemporal domains arise in numerous real-world applications, where uncovering event relationships and enabling accurate prediction are central challenges.
arXiv:2501. 14291v3 Announce Type: replace Abstract: Temporal point processes (TPPs) are stochastic process models used to characterize event sequences occurring in continuous time.
The paper proposes a method for predicting future snow water equivalent (SWE) across the Western United States by first removing spatial correlations using a Gaussian Process-based linear transformation, then training a long short-term memory (LSTM) neural network on the decorrelated data. This separation of spatial and temporal components improves predictive accuracy compared to baseline models. Additionally, the authors incorporate conformal prediction to provide distribution‑free uncertainty estimates for SWE forecasts.
arXiv:2609.21382v1 Announce Type: new Abstract: Operators of service-based systems act on forecasts of how a running execution will continue, and such a forecast is actionable only if its reliability...
arXiv:2609.13345v1 Announce Type: cross Abstract: Probabilistic forecasting is central to decision-making under uncertainty, yet its methodological landscape has become increasingly fragmented across...
The paper introduces ISBO, a scalable Spatio‑Temporal Bayesian Optimization framework that models log‑intensity using a Log‑Gaussian Cox Process and performs inference via the INLA‑SPDE approach. By employing a Matern field on meshes, ISBO obtains a sparse Gaussian Markov Random Field, enabling fast and accurate posterior inference during sequential optimization. Experiments on synthetic and real‑world datasets demonstrate accurate peak discovery, intensity recovery, and significant speedups compared to an RKHS‑based baseline.
arXiv:2607. 11177v1 Announce Type: new Abstract: In this paper, we propose deep learning based NeuroMem-FHP framework for estimating the parameters of the fractional Hawkes process (FHP), a self-exciting point process that captures long-range dependence through a fractional Mittag-Leffler excitation kernel.
arXiv:2606. 01273v1 Announce Type: new Abstract: Spatio-temporal point processes (STPPs) provide a principled framework for modeling asynchronous events in continuous time and space.
arXiv:2603.10828v2 Announce Type: replace-cross Abstract: The Segment Anything Model (SAM) has revolutionized interactive segmentation through spatial prompting. While existing work primarily focuses...
arXiv:2607. 01022v1 Announce Type: new Abstract: Spatiotemporal point processes (STPPs) model event data in continuous time and space, with applications in mobility, epidemiology, and public safety.
arXiv:2606. 20538v1 Announce Type: new Abstract: Bayesian predictive inference provides a principled framework for uncertainty quantification, data efficiency, and robust generalization.