4 citations · 4 across the 8 of their papers we have counts for
4 papers · 1 filter
A computationally-tractable measure of global sensitivity for sampling-based Bayesian inference
Arina Odnoblyudova, Charita Dellaporta, François-Xavier Briol
Bayesian inference can often be sensitive to the choice of hyperparameters of the prior or likelihood, yet defining and quantifying this sensitivity in a principled and computation…
Total robustness in Bayesian Nonlinear Regression
Mengqi Chen, Charita Dellaporta, Thomas B. Berrett +1
Modern regression analyses are often undermined by covariate measurement error, misspecification of the regression model, and misspecification of the measurement error distribution…
Robust Bayesian Inference for Measurement Error Misspecification: The Berkson and Classical Cases
Charita Dellaporta, Theodoros Damoulas
Measurement error occurs when a covariate influencing a response variable is corrupted by noise. This can lead to misleading inference outcomes, particularly in problems where accu…
Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap
Charita Dellaporta, Jeremias Knoblauch, Theodoros Damoulas +1
Simulator-based models are models for which the likelihood is intractable but simulation of synthetic data is possible. They are often used to describe complex real-world phenomena…