https://terms-en.ai-term-hub.com/zh/tags/probabilistic-models/Recent content in Probabilistic Models on 中文AI术语词典Hugozh-cnSat, 18 Jul 2026 11:44:45 +0000https://terms-en.ai-term-hub.com/zh/terms/reparameterization_trick/Sat, 18 Jul 2026 11:32:14 +0000https://terms-en.ai-term-hub.com/zh/terms/reparameterization_trick/<h2 id="definition">Definition</h2> <p>重参数化技巧是变分自编码器及其他概率模型中使用的一种基本方法。它通过将随机变量表示为确定性函数和独立噪声变量的组合，使得梯度能够流经随机节点，从而允许使用反向传播算法进行优化。</p> <h3 id="summary">Summary</h3> <p>一种将随机变量与可学习参数分离的技术，旨在变分推断中实现基于梯度的优化。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>变分推断</li> <li>梯度估计</li> <li>随机节点</li> <li>可微模拟</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>训练变分自编码器 (VAE)</li> <li>贝叶斯神经网络</li> <li>概率图模型</li> </ul> <h2 id="code-example">Code Example</h2> <div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-python" data-lang="python"><span style="display:flex;"><span><span style="color:#f92672">import</span> torch </span></span><span style="display:flex;"><span>epsilon <span style="color:#f92672">=</span> torch<span style="color:#f92672">.</span>randn(<span style="color:#ae81ff">100</span>, <span style="color:#ae81ff">10</span>) </span></span><span style="display:flex;"><span>mu <span style="color:#f92672">=</span> torch<span style="color:#f92672">.</span>zeros(<span style="color:#ae81ff">100</span>, <span style="color:#ae81ff">10</span>) </span></span><span style="display:flex;"><span>sigma <span style="color:#f92672">=</span> torch<span style="color:#f92672">.</span>ones(<span style="color:#ae81ff">100</span>, <span style="color:#ae81ff">10</span>) </span></span><span style="display:flex;"><span>z <span style="color:#f92672">=</span> mu <span style="color:#f92672">+</span> sigma <span style="color:#f92672">*</span> epsilon <span style="color:#75715e"># Reparameterized sampling</span> </span></span></code></pre></div><h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/elbo-%E8%AF%81%E6%8D%AE%E4%B8%8B%E7%95%8C/">ELBO (证据下界)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E6%BD%9C%E5%9C%A8%E5%8F%98%E9%87%8F/">潜在变量</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E5%8F%8D%E5%90%91%E4%BC%A0%E6%92%AD/">反向传播</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E8%92%99%E7%89%B9%E5%8D%A1%E6%B4%9B%E4%BC%B0%E8%AE%A1/">蒙特卡洛估计</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/expectation_propagation/Sat, 18 Jul 2026 11:16:37 +0000https://terms-en.ai-term-hub.com/zh/terms/expectation_propagation/<h2 id="definition">Definition</h2> <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。</p> <h3 id="summary">Summary</h3> <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。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>后验近似</li> <li>矩匹配</li> <li>Kullback-Leibler散度</li> <li>高斯过程</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>稀疏高斯过程</li> <li>贝叶斯逻辑回归</li> <li>概率矩阵分解</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><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)</a></li> <li><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)</a></li> <li><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)</a></li> <li><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)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/bayesian_interpretation_of_kernel_regularization/Sat, 18 Jul 2026 11:08:51 +0000https://terms-en.ai-term-hub.com/zh/terms/bayesian_interpretation_of_kernel_regularization/<h2 id="definition">Definition</h2> <p>这一概念确立了：使用特定核函数最小化正则化风险泛函，等价于在贝叶斯框架中寻找最大后验概率（MAP）估计。具体来说，它揭示了确定性核方法与概率性高斯过程之间的深层联系。</p> <h3 id="summary">Summary</h3> <p>一个理论框架，将支持向量机等核方法与高斯过程在贝叶斯先验假设下联系起来。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>高斯过程</li> <li>最大后验估计</li> <li>正则化作为先验</li> <li>核方法</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>理解支持向量机的理论基础</li> <li>将高斯过程技术应用于核回归</li> <li>为确定性模型推导不确定性估计</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><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)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/support_vector_machines-%E6%94%AF%E6%8C%81%E5%90%91%E9%87%8F%E6%9C%BA/">support_vector_machines (支持向量机)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/regularization-%E6%AD%A3%E5%88%99%E5%8C%96/">regularization (正则化)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/prior_distribution-%E5%85%88%E9%AA%8C%E5%88%86%E5%B8%83/">prior_distribution (先验分布)</a></li> </ul>