16 citations · 19 across the 7 of their papers we have counts for
7 papers
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…
Robust scalable initialization for Bayesian variational inference with multi-modal Laplace approximations
Wyatt Bridgman, Reese Jones, Mohammad Khalil
For predictive modeling relying on Bayesian inversion, fully independent, or ``mean-field'', Gaussian distributions are often used as approximate probability density functions in v…
Robust Bayesian state and parameter estimation framework for stochastic dynamical systems with combined time-varying and time-invariant parameters
Philippe Bisaillon, Brandon Robinson, Mohammad Khalil +3
We consider state and parameter estimation for a dynamical system having both time-varying and time-invariant parameters. It has been shown that the robustness of the Markov Chain…
The network uncertainty quantification method for propagating uncertainties in component-based systems
Kevin Carlberg, Sofia Guzzetti, Mohammad Khalil +1
This work introduces the network uncertainty quantification (NetUQ) method for performing uncertainty propagation in systems composed of interconnected components. The method assum…
Bayesian Modeling of Inconsistent Plastic Response due to Material Variability
Francesco Rizzi, Mohammad Khalil, Reese E. Jones +3
The advent of fabrication techniques such as additive manufacturing has focused attention on the considerable variability of material response due to defects and other microstructu…