<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Bayesian Methods on 中文AI术语词典</title><link>https://terms-en.ai-term-hub.com/zh/tags/bayesian-methods/</link><description>Recent content in Bayesian Methods on 中文AI术语词典</description><generator>Hugo</generator><language>zh-cn</language><lastBuildDate>Sat, 18 Jul 2026 11:44:45 +0000</lastBuildDate><atom:link href="https://terms-en.ai-term-hub.com/zh/tags/bayesian-methods/index.xml" rel="self" type="application/rss+xml"/><item><title>期望传播</title><link>https://terms-en.ai-term-hub.com/zh/terms/expectation_propagation/</link><pubDate>Sat, 18 Jul 2026 11:16:37 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/zh/terms/expectation_propagation/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>期望传播（EP）通过迭代 refine 高斯近似值来逼近难以处理的积分，从而估计真实后验分布。它最小化近似分布与真实分布之间的Kullback-Leibler散度，常用于贝叶斯推断和稀疏高斯过程。&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>一种用于估计复杂概率图模型中后验分布的近似推理算法。&lt;/p>
&lt;h2 id="key-concepts">Key Concepts&lt;/h2>
&lt;ul>
&lt;li>后验近似&lt;/li>
&lt;li>矩匹配&lt;/li>
&lt;li>Kullback-Leibler散度&lt;/li>
&lt;li>高斯过程&lt;/li>
&lt;/ul>
&lt;h2 id="use-cases">Use Cases&lt;/h2>
&lt;ul>
&lt;li>稀疏高斯过程&lt;/li>
&lt;li>贝叶斯逻辑回归&lt;/li>
&lt;li>概率矩阵分解&lt;/li>
&lt;/ul>
&lt;h2 id="related-terms">Related Terms&lt;/h2>
&lt;ul>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/variational_inference-%E5%8F%98%E5%88%86%E6%8E%A8%E6%96%AD/">variational_inference (变分推断)&lt;/a>&lt;/li>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/gaussian_processes-%E9%AB%98%E6%96%AF%E8%BF%87%E7%A8%8B/">gaussian_processes (高斯过程)&lt;/a>&lt;/li>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/bayesian_inference-%E8%B4%9D%E5%8F%B6%E6%96%AF%E6%8E%A8%E6%96%AD/">bayesian_inference (贝叶斯推断)&lt;/a>&lt;/li>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/mean_field_approximation-%E5%B9%B3%E5%9D%87%E5%9C%BA%E8%BF%91%E4%BC%BC/">mean_field_approximation (平均场近似)&lt;/a>&lt;/li>
&lt;/ul></description></item></channel></rss>