25 citations · 27 across the 19 of their papers we have counts for
5 papers · 1 filter
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…
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…
Accounting for variable detection functions in temporal abundance modeling via transfer learning
Kevin M. Collins, Erin M. Schliep, Tyler Wagner +1
Relative abundance, measured as the number of animals caught per unit of sampling effort (CPUE), is commonly used to monitor fish and wildlife populations, largely because sampling…
Improving ecological inference and uncertainty quantification from camera trap data through the fusion of AI confidences and manual annotations
Adira Cohen, Erin M. Schliep, Roland Kays +2
Camera traps have become an important tool in ecological research, enabling large-scale, noninvasive monitoring of wildlife populations and behavior. By automatically recording ani…
Modeling Animal Communication Using Multivariate Hawkes Processes with Additive Excitation and Multiplicative Inhibition
Bokgyeong Kang, Erin M. Schliep, Alan E. Gelfand +2
Animal acoustic communication often exhibits temporal dependence, with calls triggering or suppressing subsequent calls within and across call types, individuals, or species. While…