44 citations · 77 across the 13 of their papers we have counts for
8 papers · 1 filter
Bayesian Hierarchical Models For Multi-type Survey Data Using Spatially Correlated Covariates Measured With Error
Saikat Nandy, Scott H. Holan, Jonathan R. Bradley +1
We introduce Bayesian hierarchical models for predicting high-dimensional tabular survey data which can be distributed from one or multiple classes of distributions (e.g., Gaussian…
A Bayesian Spatio-Temporal Level Set Dynamic Model and Application to Fire Front Propagation
Myungsoo Yoo, Christopher K. Wikle
Intense wildfires impact nature, humans, and society, causing catastrophic damage to property and the ecosystem, as well as the loss of life. Forecasting wildfire front propagation…
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…
Spatio-Temporal Models for Big Multinomial Data using the Conditional Multivariate Logit-Beta Distribution
Jonathan R. Bradley, Christopher K. Wikle, Scott H. Holan
We introduce a Bayesian approach for analyzing high-dimensional multinomial data that are referenced over space and time. In particular, the proportions associated with multinomial…