arXiv:2609.09126cs.AIcs.LG2026-09
拓展阿马里贝叶斯对偶性，为现代AI提供理论新视角
A Generalization of Amari's Bayesian Duality
- 基于贝叶斯规则的凸对偶性，重构阿马里对偶性
- 提出更普适的贝叶斯对偶框架，覆盖原版本情形
- 适合研究信息几何与贝叶斯推断的理论学者
阿马里在信息几何与机器学习领域的贡献广为人知。本文重新审视其未受足够关注的贝叶斯对偶性工作，将其与贝叶斯规则的凸对偶性相连接。利用这一联系，我们提出了阿马里贝叶斯对偶性的推广形式，并讨论其在现代人工智能中的潜在意义。
原文摘要 · Abstract (English)
Amari's contributions to information geometry and machine learning are well known. Here, we revisit Amari's work on Bayesian duality which has not received as much attention. We connect Amari's Bayesian duality to a convex duality of Bayes' rule. Using this connection, we present a generalization of Amari's Bayesian duality and discuss its relevance for modern artificial intelligence.
信息几何贝叶斯推断对偶性
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