5 citations · 13 across the 9 of their papers we have counts for
5 papers · 1 filter
Using Echo State Networks to Inform Physical Models for Fire Front Propagation
Myungsoo Yoo, Christopher K. Wikle
Wildfires can be devastating, causing significant damage to property, ecosystem disruption, and loss of life. Forecasting the evolution of wildfire boundaries is essential to real-…
A Bayesian adaptive ensemble Kalman filter for sequential state and parameter estimation
Jonathan R. Stroud, Matthias Katzfuss, Christopher K. Wikle
This paper proposes new methodology for sequential state and parameter estimation within the ensemble Kalman filter. The method is fully Bayesian and propagates the joint posterior…
Bayesian Lattice Filters for Time-Varying Autoregression and Time-Frequency Analysis
Wen-Hsi Yang, Scott H. Holan, Christopher K. Wikle
Modeling nonstationary processes is of paramount importance to many scientific disciplines including environmental science, ecology, and finance, among others. Consequently, flexib…
Mixed Effects Modeling for Areal Data that Exhibit Multivariate-Spatio-Temporal Dependencies
Jonathan R. Bradley, Scott H. Holan, Christopher K. Wikle
There are many data sources available that report related variables of interest that are also referenced over geographic regions and time; however, there are relatively few general…
Bayesian Semiparametric Hierarchical Empirical Likelihood Spatial Models
Aaron T. Porter, Scott H. Holan, Christopher K. Wikle
We introduce a general hierarchical Bayesian framework that incorporates a flexible nonparametric data model specification through the use of empirical likelihood methodology, whic…