On Concentration and Revisited Large Deviations Analysis of Binary Hypothesis Testing

2011/11/08 by Igal Sason, Sason, Igal
Computer Science · Engineering · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Information Theory (cs.IT) #Statistical Methods and Inference #Wireless Communication Security Techniques #cs.IT #math.IT

paper · pdf · doi:10.48550/arxiv.1111.1992

This paper (7 pages) is closely related to a presentation at the 2012 Workshop on Information Theory and Applications, La Jolla, California, USA, February 2012

openalex publication_date 2011/11/08 · arxiv created 2012/07/15 · arxiv updated 2012/07/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper first introduces a refined version of the Azuma-Hoeffding inequality for discrete-parameter martingales with uniformly bounded jumps. The refined inequality is used to revisit the large deviations analysis of binary hypothesis testing.

Related

• Concentration of measure without independence: a unified approach via the martingale method
• Gaussian martingale inequality applies to random functions and maxima of empirical processes
• On the tradeoff between almost sure error tolerance and mean deviation frequency in martingale convergence
• Concentration inequality using unconfirmed knowledge
• On Catoni's M-Estimation