2 papers
stat.ME2026
Surrogate-based Bayesian calibration methods for chaotic systems: a comparison of traditional and non-traditional approaches
Maike F. Holthuijzen, Atlanta Chakraborty, Elizabeth Krath +1
Parameter calibration is essential for reducing uncertainty and improving predictive fidelity in physics-based models, yet it is often limited by the high computational cost of mod…
stat.CO2024
A Likelihood-Free Approach to Goal-Oriented Bayesian Optimal Experimental Design
Atlanta Chakraborty, Xun Huan, Tommie Catanach
Conventional Bayesian optimal experimental design seeks to maximize the expected information gain (EIG) on model parameters. However, the end goal of the experiment often is not to…