Extending Bhattacharyya Coefficients to Power Means for Bayes Error Bounds
FrnkNlsn · x · 2026-08-10
This post highlights a paper exploring the Bhattacharyya similarity coefficient and its relationship with various statistical divergences (e.g., α-divergences, Rényi, Hellinger).
The paper "Generalized Bhattacharyya and Chernoff upper bounds on Bayes error using quasi-arithmetic means" notes that the traditional Bhattacharyya coefficient is essentially a weighted geometric mean. The authors extend this concept to generalized power means to derive new upper bounds for Bayes error, introducing novel notions of statistical divergences and affinity coefficients as a byproduct.
More from Research
- Indie Dev Builds LLM Consensus Leaderboard Using Esports Ranking Algorithms — 数字生命卡兹克 · 2026-08-10
- ICML Paper: Forcing LLMs to 'Overthink' Leaks Their Hidden Knowledge — PandaAshwinee · 2026-08-10
- GUIDE System Dynamically Generates Multimodal Interactions to Reduce Stress, UIST Paper — _Hao_Zhu · 2026-08-10
- Crime Economists Host Hackathon to Batch Generate Paper Drafts with AI — paulnovosad · 2026-08-10
- PhyLatent: Optimizing JEPA World Model Representations for Better Robot Control — burny_tech · 2026-08-10
- CISPO post-training algorithm released for improved model alignment — Sauers_ · 2026-08-10