research
∙
07/14/2022
A Spatio-Temporal Dirichlet Process Mixture Model for Coronavirus Disease-19
Understanding the spatio-temporal patterns of the coronavirus disease 20...
share
research
∙
10/20/2021
Bayesian Model Calibration and Sensitivity Analysis for Oscillating Biological Experiments
Most organisms exhibit various endogenous oscillating behaviors which pr...
research
∙
01/18/2021
Fast and accurate learned multiresolution dynamical downscaling for precipitation
This study develops a neural network-based approach for emulating high-r...
research
∙
08/29/2020
Computer Model Calibration with Time Series Data using Deep Learning and Quantile Regression
Computer models play a key role in many scientific and engineering probl...
research
∙
07/31/2019
Ice Model Calibration Using Semi-continuous Spatial Data
Rapid changes in Earth's cryosphere caused by human activity can lead to...
research
∙
11/24/2018
A Regularized Spatial Market Segmentation Method with Dirichlet Process Gaussian Mixture Prior
Spatially referenced data are increasingly available thanks to the devel...
research
∙
10/15/2018
Computer model calibration based on image warping metrics: an application for sea ice deformation
Arctic sea ice plays an important role in the global climate. Sea ice mo...
research
∙
12/12/2017