25 citations · 27 across the 19 of their papers we have counts for
7 papers · 1 filter
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…
Efficient Bayesian Inference for Spatial Point Patterns Using the Palm Likelihood
Kevin M. Collins, Erin M. Schliep
Bayesian inference for spatial point patterns is often hindered computationally by intractable likelihoods. In the frequentist literature, estimating equations utilizing pseudolike…
Spatial causal inference in the presence of preferential sampling to study the impacts of marine protected areas
Dongjae Son, Brian J. Reich, Erin M. Schliep +2
Marine Protected Areas (MPAs) have been established globally to conserve marine resources. Given their maintenance costs and impact on commercial fishing, it is critical to evaluat…
A Review of Data-Driven Discovery for Dynamic Systems
Joshua S. North, Christopher K. Wikle, Erin M. Schliep
Many real-world scientific processes are governed by complex nonlinear dynamic systems that can be represented by differential equations. Recently, there has been increased interes…
A Bayesian Approach for Spatio-Temporal Data-Driven Dynamic Equation Discovery
Joshua S. North, Christopher K. Wikle, Erin M. Schliep
Differential equations based on physical principals are used to represent complex dynamic systems in all fields of science and engineering. Through repeated use in both academics a…
Correcting spatial Gaussian process parameter and prediction variance estimation under informative sampling
Erin M. Schliep, Christopher K. Wikle, Ranadeep Daw
Informative sampling designs can impact spatial prediction, or kriging, in two important ways. First, the sampling design can bias spatial covariance parameter estimation, which in…