Measuring Bayesian Robustness Using Rényi Divergence

2019/05/15 by Luai Al‐Labadi, Ce Wang, Al-Labadi, Luai +1
Decision Sciences · Mathematics · #62F15 #62F35 #62G35 #Advanced Statistical Methods and Models #Advanced Statistical Process Monitoring #FOS: Mathematics #Statistical Methods and Inference #Statistics Theory (math.ST)

paper · pdf · doi:10.48550/arxiv.1905.05945

openalex publication_date 2019/05/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper deals with measuring the Bayesian robustness of classes of contaminated priors. Two different classes of priors in the neighborhood of the elicited prior are considered. The first one is the well-known ε-contaminated class, while the second one is the geometric mixing class. The proposed measure of robustness is based on computing the curvature of Rényi divergence between posterior distributions. Examples are used to illustrate the results by using simulated and real data sets.

Citations

• Rényi Divergence Variational Inference

Related

• Local and global robustness in conjugate Bayesian analysis
• Adaptation of the Tuning Parameter in General Bayesian Inference with Robust Divergence
• Likelihood distortion and Bayesian local robustness
• Posterior Robustness with Milder Conditions: Contamination Models Revisited
• Bayes Sensitivity with Fisher-Rao Metric