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08/08/2023
Calibrated Forecasts of Quasi-Periodic Climate Processes with Deep Echo State Networks and Penalized Quantile Regression
Among the most relevant processes in the Earth system for human habitabi...
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06/07/2023
Bayesian Ensemble Echo State Networks for Enhancing Binary Stochastic Cellular Automata
Binary spatio-temporal data are common in many application areas. Such d...
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06/05/2023
Data-Driven Modeling of Wildfire Spread with Stochastic Cellular Automata and Latent Spatio-Temporal Dynamics
We propose a Bayesian stochastic cellular automata modeling approach to ...
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02/09/2023
Using Echo State Networks to Inform Physical Models for Fire Front Propagation
Wildfires can be devastating, causing significant damage to property, ec...
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11/17/2022
Bayesian Hierarchical Models For Multi-type Survey Data Using Spatially Correlated Covariates Measured With Error
We introduce Bayesian hierarchical models for predicting high-dimensiona...
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11/09/2022
REDS: Random Ensemble Deep Spatial prediction
There has been a great deal of recent interest in the development of spa...
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10/26/2022
A Bayesian Spatio-Temporal Level Set Dynamic Model and Application to Fire Front Propagation
Intense wildfires impact nature, humans, and society, causing catastroph...
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10/19/2022
A Review of Data-Driven Discovery for Dynamic Systems
Many real-world scientific processes are governed by complex nonlinear d...
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09/06/2022
A Bayesian Approach for Spatio-Temporal Data-Driven Dynamic Equation Discovery
Differential equations based on physical principals are used to represen...
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06/05/2022
Statistical Deep Learning for Spatial and Spatio-Temporal Data
Deep neural network models have become ubiquitous in recent years, and h...
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09/21/2021
A Bayesian Hidden Semi-Markov Model with Covariate-Dependent State Duration Parameters for High-Frequency Environmental Data
Environmental time series data observed at high frequencies can be studi...
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08/27/2021
Correcting spatial Gaussian process parameter and prediction variance estimation under informative sampling
Informative sampling designs can impact spatial prediction, or kriging, ...
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10/21/2020
A Bayesian Hidden Semi-Markov Model with Covariate-Dependent State Duration Parameters for High-Frequency Data from Wearable Devices
Data collected by wearable devices in sports provide valuable informatio...
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10/07/2020
A multi-surrogate higher-order singular value decomposition tensor emulator for spatio-temporal simulators
We introduce methodology to construct an emulator for environmental and ...
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09/08/2020
Bayesian Inverse Reinforcement Learning for Collective Animal Movement
Agent-based methods allow for defining simple rules that generate comple...
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03/15/2020
On the spatial and temporal shift in the archetypal seasonal temperature cycle as driven by annual and semi-annual harmonics
Statistical methods are required to evaluate and quantify the uncertaint...
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10/29/2019
Deep Integro-Difference Equation Models for Spatio-Temporal Forecasting
Integro-difference equation (IDE) models describe the conditional depend...
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08/07/2019
The utility of a Bayesian Markov model with Pólya-Gamma sampling for estimating individual behavior transition probabilities from accelerometer classifications
The use of accelerometers in wildlife tracking provides a fine-scale dat...
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04/27/2019
An R Package for Spatio-Temporal Change of Support
Spatio-temporal change of support (STCOS) methods are designed for stati...
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02/22/2019
Comparison of Deep Neural Networks and Deep Hierarchical Models for Spatio-Temporal Data
Spatio-temporal data are ubiquitous in the agricultural, ecological, and...
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12/09/2018
Spatio-Temporal Models for Big Multinomial Data using the Conditional Multivariate Logit-Beta Distribution
We introduce a Bayesian approach for analyzing high-dimensional multinom...
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11/20/2018
A Hierarchical Spatio-Temporal Statistical Model for Physical Systems
In this paper, we extend and analyze a Bayesian hierarchical spatio-temp...
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06/28/2018
Hierarchical (Deep) Echo State Networks with Uncertainty Quantification for Spatio-Temporal Forecasting
Long-lead forecasting for spatio-temporal problems can often entail comp...
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02/07/2018
Interpolating Distributions for Populations in Nested Geographies using Public-use Data with Application to the American Community Survey
Statistical agencies often publish multiple data products from the same ...
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11/02/2017
Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data
Recurrent neural networks (RNNs) are nonlinear dynamical models commonly...
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08/16/2017
