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stat.ME2026
Divide-and-Conquer Neural Bayes Estimation for Spatial and Spatiotemporal Point Process Models
Bokgyeong Kang, Andrew Zammit-Mangion, Erin M. Schliep +1
Spatial and spatiotemporal point process models are widely used to analyze event data across many scientific disciplines, but likelihood-based Bayesian inference is often computati…
stat.ME2026
Analyzing spatial point processes degraded by displacement and imperfect detection
Kevin M. Collins, Erin M. Schliep, Alan E. Gelfand +3
Spatial point processes are a valuable tool for probabilistic modeling to explain location data. However, the data themselves are often observed imperfectly. In order to perform ac…