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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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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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05/18/2020
Distributed lag models to identify the cumulative effects of training and recovery in athletes using multivariate ordinal wellness data
Subjective wellness data can provide important information on the well-b...
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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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03/02/2020
Long-term Spatial Modeling for Characteristics of Extreme Heat Events
There is increasing evidence that global warming manifests itself in mor...
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06/17/2019
