Among the most relevant processes in the Earth system for human habitabi...
Binary spatio-temporal data are common in many application areas. Such d...
We propose a Bayesian stochastic cellular automata modeling approach to ...
Wildfires can be devastating, causing significant damage to property,
ec...
We introduce Bayesian hierarchical models for predicting high-dimensiona...
There has been a great deal of recent interest in the development of spa...
Intense wildfires impact nature, humans, and society, causing catastroph...
Many real-world scientific processes are governed by complex nonlinear
d...
Differential equations based on physical principals are used to represen...
Deep neural network models have become ubiquitous in recent years, and h...
Environmental time series data observed at high frequencies can be studi...
Informative sampling designs can impact spatial prediction, or kriging, ...
Data collected by wearable devices in sports provide valuable informatio...
We introduce methodology to construct an emulator for environmental and
...
Agent-based methods allow for defining simple rules that generate comple...
Statistical methods are required to evaluate and quantify the uncertaint...
Integro-difference equation (IDE) models describe the conditional depend...
The use of accelerometers in wildlife tracking provides a fine-scale dat...
Spatio-temporal change of support (STCOS) methods are designed for
stati...
Spatio-temporal data are ubiquitous in the agricultural, ecological, and...
We introduce a Bayesian approach for analyzing high-dimensional multinom...
In this paper, we extend and analyze a Bayesian hierarchical spatio-temp...
Long-lead forecasting for spatio-temporal problems can often entail comp...
Statistical agencies often publish multiple data products from the same
...
Recurrent neural networks (RNNs) are nonlinear dynamical models commonly...
Spatio-temporal data and processes are prevalent across a wide variety o...