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06/28/2023
Spatiotemporal Besov Priors for Bayesian Inverse Problems
Fast development in science and technology has driven the need for prope...
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10/14/2022
Bayesian Regularization on Function Spaces via Q-Exponential Process
Regularization is one of the most important topics in optimization, stat...
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09/26/2022
Sampling Constrained Continuous Probability Distributions: A Review
The problem of sampling constrained continuous distributions has frequen...
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04/22/2022
Bayesian Spatiotemporal Modeling for Inverse Problems
Inverse problems with spatiotemporal observations are ubiquitous in scie...
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01/11/2021
Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks
Due to the importance of uncertainty quantification (UQ), Bayesian appro...
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01/10/2020
Calibrate, Emulate, Sample
Many parameter estimation problems arising in applications are best cast...
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10/13/2019
Deep Markov Chain Monte Carlo
We propose a new computationally efficient sampling scheme for Bayesian ...
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01/13/2019
Learning Temporal Evolution of Spatial Dependence with Generalized Spatiotemporal Gaussian Process Models
Spatiotemporal processes are ubiquitous in our life and have been a tren...
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07/15/2018
Adaptive Dimension Reduction to Accelerate Infinite-Dimensional Geometric Markov Chain Monte Carlo
Bayesian inverse problems highly rely on efficient and effective inferen...
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11/08/2017
Flexible Bayesian Dynamic Modeling of Correlation and Covariance Matrices
Modeling correlation (and covariance) matrices is a challenging problem ...
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08/31/2017
Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations
Climate projections continue to be marred by large uncertainties, which ...
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06/19/2015
Sampling constrained probability distributions using Spherical Augmentation
Statistical models with constrained probability distributions are abunda...
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01/19/2012