1 citations · 1 across the 4 of their papers we have counts for
5 papers
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…
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…
Modularized Bayesian analyses and cutting feedback in likelihood-free inference
Atlanta Chakraborty, David J. Nott, Christopher Drovandi +2
There has been much recent interest in modifying Bayesian inference for misspecified models so that it is useful for specific purposes. One popular modified Bayesian inference meth…
Weakly informative priors and prior-data conflict checking for likelihood-free inference
Atlanta Chakraborty, David J. Nott, Michael Evans
Bayesian likelihood-free inference, which is used to perform Bayesian inference when the likelihood is intractable, enjoys an increasing number of important scientific applications…
A robust and non-parametric model for prediction of dengue incidence
Atlanta Chakraborty, Vijay Chandru
Disease surveillance is essential not only for the prior detection of outbreaks but also for monitoring trends of the disease in the long run. In this paper, we aim to build a tact…