<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>MCMC算法 on 办公AI智能小助手</title>
    <link>https://blog.qife122.com/tags/mcmc%E7%AE%97%E6%B3%95/</link>
    <description>Recent content in MCMC算法 on 办公AI智能小助手</description>
    <generator>Hugo</generator>
    <language>zh-cn</language>
    <copyright>qife</copyright>
    <lastBuildDate>Mon, 08 Sep 2025 19:46:20 +0800</lastBuildDate>
    <atom:link href="https://blog.qife122.com/tags/mcmc%E7%AE%97%E6%B3%95/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>基于Copula差异的样本质量评估技术</title>
      <link>https://blog.qife122.com/p/%E5%9F%BA%E4%BA%8Ecopula%E5%B7%AE%E5%BC%82%E7%9A%84%E6%A0%B7%E6%9C%AC%E8%B4%A8%E9%87%8F%E8%AF%84%E4%BC%B0%E6%8A%80%E6%9C%AF/</link>
      <pubDate>Mon, 08 Sep 2025 19:46:20 +0800</pubDate>
      <guid>https://blog.qife122.com/p/%E5%9F%BA%E4%BA%8Ecopula%E5%B7%AE%E5%BC%82%E7%9A%84%E6%A0%B7%E6%9C%AC%E8%B4%A8%E9%87%8F%E8%AF%84%E4%BC%B0%E6%8A%80%E6%9C%AF/</guid>
      <description>&lt;h1 id=&#34;测量样本质量的copula差异方法&#34;&gt;测量样本质量的Copula差异方法&lt;/h1&gt;&#xA;&lt;p&gt;现代贝叶斯机器学习中可扩展的马尔可夫链蒙特卡洛（MCMC）算法（如随机梯度Langevin动力学-SGLD）为了计算速度牺牲了渐近精确性，从而产生了一个关键的诊断缺口：当应用于有偏采样器时，传统的样本质量测量方法会灾难性地失效。&lt;/p&gt;</description>
    </item>
  </channel>
</rss>
