91 citations · 386 across the 54 of their papers we have counts for
60 papers
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…
Modeling Spatio-temporal Extremes via Conditional Variational Autoencoders
Xiaoyu Ma, Likun Zhang, Christopher K. Wikle
Extreme weather events are widely studied in fields such as agriculture, ecology, and meteorology. The spatio-temporal co-occurrence of extreme events can strengthen or weaken unde…
Modeling high and low extremes with a novel dynamic spatio-temporal model
Myungsoo Yoo, Likun Zhang, Christopher K. Wikle +1
Extreme environmental events such as severe storms, drought, heat waves, flash floods, and abrupt species collapse have become more prevalent in the earth-atmosphere dynamic system…
A Statistician's Overview of Physics-Informed Neural Networks for Spatio-Temporal Data
Christopher K. Wikle, Joshua North, Giri Gopalan +1
The recent success of deep neural network models with physical constraints (so-called, Physics-Informed Neural Networks, PINNs) has led to renewed interest in the incorporation of…
An Anytime Valid Test for Complete Spatial Randomness
Vaidehi Dixit, Christopher K. Wikle, Scott H. Holan
A relevant question when analyzing spatial point patterns is that of spatial randomness. More specifically, before any model can be fit to a point pattern a first step is to test t…
Inference for Log-Gaussian Cox Point Processes using Bayesian Deep Learning: Application to Human Oral Microbiome Image Data
Shuwan Wang, Christopher K. Wikle, Athanasios C. Micheas +3
It is common in nature to see aggregation of objects in space. Exploring the mechanism associated with the locations of such clustered observations can be essential to understandin…