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A Bayesian model calibration framework for stochastic compartmental models with both time-varying and time-invariant parameters
Brandon Robinson, Philippe Bisaillon, Jodi D. Edwards +4
We consider state and parameter estimation for compartmental models having both time-varying and time-invariant parameters. Though the described Bayesian computational framework is…
Sparse Bayesian neural networks for regression: Tackling overfitting and computational challenges in uncertainty quantification
Nastaran Dabiran, Brandon Robinson, Rimple Sandhu +3
Neural networks (NNs) are primarily developed within the frequentist statistical framework. Nevertheless, frequentist NNs lack the capability to provide uncertainties in the predic…
Exploring hierarchical framework of nonlinear sparse Bayesian learning algorithm through numerical investigations
Nastaran Dabiran, Brandon Robinson, Rimple Sandhu +4
Sparse Bayesian learning (SBL) has been extensively utilized in data-driven modeling to combat the issue of overfitting. While SBL excels in linear-in-parameter models, its direct…
Domain Decomposition of Stochastic PDEs: Development of Probabilistic Wirebasket-based Two-level Preconditioners
Ajit Desai, Mohammad Khalil, Chris L. Pettit +2
Realistic physical phenomena exhibit random fluctuations across many scales in the input and output processes. Models of these phenomena require stochastic PDEs. For three-dimensio…