<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>概率模型 on AI 早报</title><link>https://ai-news.example.com/tags/%E6%A6%82%E7%8E%87%E6%A8%A1%E5%9E%8B/</link><description>Recent content in 概率模型 on AI 早报</description><generator>Hugo</generator><language>zh-CN</language><lastBuildDate>Mon, 06 Jul 2026 23:16:58 +0800</lastBuildDate><atom:link href="https://ai-news.example.com/tags/%E6%A6%82%E7%8E%87%E6%A8%A1%E5%9E%8B/feed.xml" rel="self" type="application/rss+xml"/><item><title>[EN]AutoBNN：用组合贝叶斯神经网络实现自动化概率时间序列预测</title><link>https://ai-news.example.com/articles/enautobnn/</link><pubDate>Mon, 06 Jul 2026 23:16:58 +0800</pubDate><guid>https://ai-news.example.com/articles/enautobnn/</guid><description>Google 发布了开源工具 AutoBNN，将传统概率模型的可解释性与神经网络的可扩展性相结合，自动发现时间序列预测模型，并提供高质量的置信区间。</description></item></channel></rss>