On Concentration and Revisited Large Deviations Analysis of Binary Hypothesis Testing
Igal Sason
TL;DR
This paper refines a key inequality for martingales and applies it to improve the large deviations analysis in binary hypothesis testing, enhancing understanding of error probabilities.
Contribution
It introduces a refined Azuma-Hoeffding inequality and revisits large deviations analysis in binary hypothesis testing using this new tool.
Findings
Refined Azuma-Hoeffding inequality for martingales
Improved large deviations bounds in binary hypothesis testing
Enhanced theoretical understanding of error probabilities
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.
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Taxonomy
TopicsStatistical Methods and Inference · Bayesian Methods and Mixture Models · Wireless Communication Security Techniques