Robust Bayesian

Robust Bayesian methods aim to improve the reliability and accuracy of Bayesian inference by mitigating the impact of noise, outliers, and model misspecification. Current research focuses on developing robust likelihood models, often employing techniques like optimal transport, correntropy, and H-infinity norm optimization, to handle non-Gaussian noise and distributional shifts, as well as incorporating data-driven calibration strategies. These advancements enhance the applicability of Bayesian methods in diverse fields, including signal processing, machine learning, and causal inference, by providing more reliable and calibrated uncertainty estimates even under challenging conditions.