Divide-and-Conquer Neural Bayes Estimation for Spatial and Spatiotemporal Point Process Models

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.

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