Probability on 中文AI术语词典https://terms-en.ai-term-hub.com/zh/tags/probability/Recent content in Probability on 中文AI术语词典Hugozh-cnSat, 18 Jul 2026 11:44:45 +0000多臂老虎机https://terms-en.ai-term-hub.com/zh/terms/multi_armed_bandit/Sat, 18 Jul 2026 11:26:49 +0000https://terms-en.ai-term-hub.com/zh/terms/multi_armed_bandit/<h2 id="definition">Definition</h2> <p>多臂老虎机问题说明了智能体面临的困境：是坚持使用已知的奖励选项（利用），还是尝试新选项以发现可能带来更高奖励的策略。该问题旨在优化长期累积奖励。</p> <h3 id="summary">Summary</h3> <p>多臂老虎机是概率论和强化学习中的一个经典问题，用于模拟探索与利用之间的权衡。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>探索与利用的权衡</li> <li>奖励最大化</li> <li>随机过程</li> <li>遗憾最小化</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>A/B测试优化</li> <li>推荐系统</li> <li>广告展示策略</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/reinforcement-learning-%E5%BC%BA%E5%8C%96%E5%AD%A6%E4%B9%A0/">Reinforcement Learning (强化学习)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/epsilon-greedy-%CE%B5-%E8%B4%AA%E5%BF%83%E7%AE%97%E6%B3%95/">Epsilon-Greedy (ε-贪心算法)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/contextual-bandits-%E4%B8%8A%E4%B8%8B%E6%96%87%E8%80%81%E8%99%8E%E6%9C%BA/">Contextual Bandits (上下文老虎机)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/optimization-%E4%BC%98%E5%8C%96/">Optimization (优化)</a></li> </ul>核密度估计https://terms-en.ai-term-hub.com/zh/terms/kernel_density_estimation/Sat, 18 Jul 2026 11:22:59 +0000https://terms-en.ai-term-hub.com/zh/terms/kernel_density_estimation/<h2 id="definition">Definition</h2> <p>核密度估计（KDE）是一种基本的统计技术，通过对离散数据点进行平滑处理，生成连续的概率分布曲线。它在每个数据点处放置一个核函数（通常为高斯核），并将这些核函数叠加起来，从而估计出潜在的概率密度函数。</p> <h3 id="summary">Summary</h3> <p>一种非参数方法，用于根据有限的数据样本来估计随机变量的概率密度函数。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>概率密度函数</li> <li>非参数统计</li> <li>平滑处理</li> <li>高斯核</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>探索性数据分析 (EDA)</li> <li>单变量数据中的异常检测</li> <li>可视化数据集中的特征分布</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">from</span> scipy.stats <span style="color:#f92672">import</span> gaussian_kde </span></span><span style="display:flex;"><span><span style="color:#f92672">import</span> numpy <span style="color:#66d9ef">as</span> np </span></span><span style="display:flex;"><span> </span></span><span style="display:flex;"><span>data <span style="color:#f92672">=</span> np<span style="color:#f92672">.</span>random<span style="color:#f92672">.</span>normal(<span style="color:#ae81ff">0</span>, <span style="color:#ae81ff">1</span>, <span style="color:#ae81ff">100</span>) </span></span><span style="display:flex;"><span>kde <span style="color:#f92672">=</span> gaussian_kde(data) </span></span><span style="display:flex;"><span>x_vals <span style="color:#f92672">=</span> np<span style="color:#f92672">.</span>linspace(<span style="color:#f92672">-</span><span style="color:#ae81ff">3</span>, <span style="color:#ae81ff">3</span>, <span style="color:#ae81ff">100</span>) </span></span><span style="display:flex;"><span>y_vals <span style="color:#f92672">=</span> kde(x_vals) </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/%E7%9B%B4%E6%96%B9%E5%9B%BE-histogram/">直方图 (Histogram)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E5%B8%95%E5%B0%94%E6%B3%BD%E7%AA%97-parzen-window/">帕尔泽窗 (Parzen Window)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E5%B8%A6%E5%AE%BD%E9%80%89%E6%8B%A9-bandwidth-selection/">带宽选择 (Bandwidth Selection)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/scipy%E7%BB%9F%E8%AE%A1%E6%A8%A1%E5%9D%97-scipy-stats/">SciPy统计模块 (Scipy Stats)</a></li> </ul>Inductive Probabilityhttps://terms-en.ai-term-hub.com/zh/terms/inductive_probability/Sat, 18 Jul 2026 11:22:14 +0000https://terms-en.ai-term-hub.com/zh/terms/inductive_probability/<h2 id="definition">Definition</h2> <p>归纳概率量化了在观察到的证据下假设为真的可能性，承认结论是概率性的而非确定性的。它是贝叶斯推断的基础，在贝叶斯推断中&hellip;</p> <h3 id="summary">Summary</h3> <p>衡量证据支持假设程度的指标，区别于演绎确定性。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>贝叶斯推断</li> <li>不确定性</li> <li>证据</li> <li>概率论</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>贝叶斯网络</li> <li>风险评估</li> <li>医学诊断推理</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/bayes-theorem-%E8%B4%9D%E5%8F%B6%E6%96%AF%E5%AE%9A%E7%90%86/">Bayes&rsquo; Theorem (贝叶斯定理)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/prior-probability-%E5%85%88%E9%AA%8C%E6%A6%82%E7%8E%87/">Prior Probability (先验概率)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/posterior-probability-%E5%90%8E%E9%AA%8C%E6%A6%82%E7%8E%87/">Posterior Probability (后验概率)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/statistical-inference-%E7%BB%9F%E8%AE%A1%E6%8E%A8%E6%96%AD/">Statistical Inference (统计推断)</a></li> </ul>基于流的生成模型https://terms-en.ai-term-hub.com/zh/terms/flow_based_generative_model/Sat, 18 Jul 2026 11:17:25 +0000https://terms-en.ai-term-hub.com/zh/terms/flow_based_generative_model/<h2 id="definition">Definition</h2> <p>基于流的生成模型通过对简单的基分布（如高斯分布）应用一系列可逆且可微的变换来构建复杂的概率分布。由于变换是可逆的，模型可以精确计算似然值，便于密度估计和采样。</p> <h3 id="summary">Summary</h3> <p>一类利用可逆变换将简单分布映射到复杂数据分布的生成模型。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>可逆变换</li> <li>精确似然</li> <li>归一化流</li> <li>变量变换</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>图像生成</li> <li>密度估计</li> <li>异常检测</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/normalizing-flow-%E5%BD%92%E4%B8%80%E5%8C%96%E6%B5%81/">Normalizing Flow (归一化流)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/gan-%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E7%BD%91%E7%BB%9C/">GAN (生成对抗网络)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/vae-%E5%8F%98%E5%88%86%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8/">VAE (变分自编码器)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/coupling-layer-%E8%80%A6%E5%90%88%E5%B1%82/">Coupling Layer (耦合层)</a></li> </ul>基于能量的模型https://terms-en.ai-term-hub.com/zh/terms/energy_based_model/Sat, 18 Jul 2026 11:16:12 +0000https://terms-en.ai-term-hub.com/zh/terms/energy_based_model/<h2 id="definition">Definition</h2> <p>基于能量的模型（EBM）使用源自能量函数的未归一化密度函数来定义输入数据的概率分布。能量函数将数据点映射到实数，合理的配置具有较低的能量，而不合理的配置具有较高的能量。模型通常通过马尔可夫链蒙特卡洛方法进行采样和训练。</p> <h3 id="summary">Summary</h3> <p>一种概率模型，为合理的配置分配低能量值，为不合理的配置分配高能量值。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>未归一化概率</li> <li>配分函数</li> <li>能量函数</li> <li>马尔可夫链蒙特卡洛</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>图像生成与修复</li> <li>密度估计</li> <li>异常检测</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/boltzmann-machine-%E7%8E%BB%E5%B0%94%E5%85%B9%E6%9B%BC%E6%9C%BA/">Boltzmann machine (玻尔兹曼机)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/deep-boltzmann-machine-%E6%B7%B1%E5%BA%A6%E7%8E%BB%E5%B0%94%E5%85%B9%E6%9B%BC%E6%9C%BA/">Deep Boltzmann machine (深度玻尔兹曼机)</a></li> <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/probabilistic-graphical-models-%E6%A6%82%E7%8E%87%E5%9B%BE%E6%A8%A1%E5%9E%8B/">Probabilistic graphical models (概率图模型)</a></li> </ul>布拉德利-特里模型https://terms-en.ai-term-hub.com/zh/terms/bradleyterry_model/Sat, 18 Jul 2026 11:09:30 +0000https://terms-en.ai-term-hub.com/zh/terms/bradleyterry_model/<h2 id="definition">Definition</h2> <p>布拉德利-特里模型是一种概率模型，广泛用于心理测量学和机器学习中以处理成对比较。它为每个项目分配一个潜在分数，计算概率……</p> <h3 id="summary">Summary</h3> <p>一种用于分析成对比较数据的统计模型，估计一个项目优于另一个项目的概率。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>成对比较</li> <li>排名系统</li> <li>逻辑回归</li> <li>偏好学习</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>体育排名算法</li> <li>A/B测试分析</li> <li>基于人类反馈的强化学习 (RLHF)</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/elo-rating-elo-%E7%AD%89%E7%BA%A7%E5%88%86/">Elo Rating ( Elo 等级分)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/plackett-luce-model-%E6%99%AE%E6%8B%89%E5%88%87%E7%89%B9-%E5%8D%A2%E6%96%AF%E6%A8%A1%E5%9E%8B/">Plackett-Luce Model (普拉切特-卢斯模型)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/isotonic-regression-%E4%BF%9D%E5%BA%8F%E5%9B%9E%E5%BD%92/">Isotonic Regression (保序回归)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/rlhf-%E5%9F%BA%E4%BA%8E%E4%BA%BA%E7%B1%BB%E5%8F%8D%E9%A6%88%E7%9A%84%E5%BC%BA%E5%8C%96%E5%AD%A6%E4%B9%A0/">RLHF (基于人类反馈的强化学习)</a></li> </ul>基础比率https://terms-en.ai-term-hub.com/zh/terms/base_rate/Sat, 18 Jul 2026 11:08:27 +0000https://terms-en.ai-term-hub.com/zh/terms/base_rate/<h2 id="definition">Definition</h2> <p>在统计学和机器学习中，基础比率指的是给定数据集中某种条件或结果的潜在频率。忽视基础比率往往会导致基础比率谬误，即人们倾向于忽略先验概率而过度依赖具体证据。</p> <h3 id="summary">Summary</h3> <p>基础比率是指在一个群体中某事件发生的先验概率，独立于任何特定证据或测试结果。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>先验概率</li> <li>类别不平衡</li> <li>贝叶斯推断</li> <li>基础比率谬误</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>医学诊断</li> <li>欺诈检测</li> <li>风险评估</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/precision-%E7%B2%BE%E7%A1%AE%E7%8E%87/">Precision (精确率)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/recall-%E5%8F%AC%E5%9B%9E%E7%8E%87/">Recall (召回率)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/bayes-theorem-%E8%B4%9D%E5%8F%B6%E6%96%AF%E5%AE%9A%E7%90%86/">Bayes&rsquo; Theorem (贝叶斯定理)</a></li> </ul>Wasserstein（瓦瑟斯坦）https://terms-en.ai-term-hub.com/zh/terms/wasserstein/Sat, 18 Jul 2026 10:56:05 +0000https://terms-en.ai-term-hub.com/zh/terms/wasserstein/<h2 id="definition">Definition</h2> <p>Wasserstein距离，也称为地球移动距离（Earth Mover&rsquo;s Distance），通过计算将质量从一个分布移动到另一个分布所需的“最小工作量”来量化两个概率分布之间的差异。</p> <h3 id="summary">Summary</h3> <p>一种基于将一个概率分布转换为另一个所需最小成本来衡量概率分布之间距离的指标。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>地球移动距离</li> <li>概率分布</li> <li>最优传输</li> <li>梯度稳定性</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>训练稳定的生成对抗网络</li> <li>领域自适应</li> <li>测量分布相似性</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/kl%E6%95%A3%E5%BA%A6-kullback-leibler-divergence/">KL散度 (Kullback-Leibler Divergence)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E7%94%9F%E6%88%90%E5%AF%B9%E6%8A%97%E7%BD%91%E7%BB%9C-generative-adversarial-networks/">生成对抗网络 (Generative Adversarial Networks)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E6%9C%80%E4%BC%98%E4%BC%A0%E8%BE%93-optimal-transport/">最优传输 (Optimal Transport)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E5%88%86%E5%B8%83%E5%8C%B9%E9%85%8D-distribution-matching/">分布匹配 (Distribution Matching)</a></li> </ul>随机https://terms-en.ai-term-hub.com/zh/terms/stochastic/Sat, 18 Jul 2026 10:55:02 +0000https://terms-en.ai-term-hub.com/zh/terms/stochastic/<h2 id="definition">Definition</h2> <p>随机元素为人工智能系统引入变异性，例如数据中的噪声或权重的随机初始化。与确定性模型不同，随机模型考虑了不确定性，从而使得&hellip;</p> <h3 id="summary">Summary</h3> <p>描述涉及随机性和概率而非确定性结果的过程或模型。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>随机性</li> <li>概率</li> <li>不确定性</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>蒙特卡洛方法</li> <li>生成对抗网络</li> <li>贝叶斯推断</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E7%A1%AE%E5%AE%9A%E6%80%A7-deterministic/">确定性 (Deterministic)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E5%99%AA%E5%A3%B0-noise/">噪声 (Noise)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E5%88%86%E5%B8%83-distribution/">分布 (Distribution)</a></li> </ul>先验https://terms-en.ai-term-hub.com/zh/terms/prior/Sat, 18 Jul 2026 10:53:51 +0000https://terms-en.ai-term-hub.com/zh/terms/prior/<h2 id="definition">Definition</h2> <p>“先验”代表了在纳入新观测值之前，关于某个变量的现有信念或历史数据。在贝叶斯推断中，先验与观测数据的似然度相结合，以更新对该变量的认知，形成后验分布。</p> <h3 id="summary">Summary</h3> <p>在贝叶斯统计中，表示在观察新证据或数据之前，对某个参数的知识或信念的概率分布。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>贝叶斯推断</li> <li>概率分布</li> <li>正则化</li> <li>后验</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>实现贝叶斯神经网络</li> <li>应用L1/L2正则化作为先验约束</li> <li>结合新的测试结果更新医学诊断</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/posterior-%E5%90%8E%E9%AA%8C/">Posterior (后验)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/likelihood-%E4%BC%BC%E7%84%B6/">Likelihood (似然)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/bayesian-%E8%B4%9D%E5%8F%B6%E6%96%AF/">Bayesian (贝叶斯)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/probability-%E6%A6%82%E7%8E%87/">Probability (概率)</a></li> </ul>马尔可夫https://terms-en.ai-term-hub.com/zh/terms/markov/Sat, 18 Jul 2026 10:53:03 +0000https://terms-en.ai-term-hub.com/zh/terms/markov/<h2 id="definition">Definition</h2> <p>在人工智能和概率论中，马尔可夫过程是描述系统随机状态转换的基础模型。其核心原则是马尔可夫性质，即系统在下一时刻的状态仅由当前状态决定，而与过去的历史状态无关。</p> <h3 id="summary">Summary</h3> <p>马尔可夫指代随机过程，其中未来状态仅取决于当前状态，遵循无记忆性的马尔可夫性质。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>马尔可夫性质</li> <li>无记忆性</li> <li>状态转换</li> <li>随机过程</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>强化学习智能体</li> <li>自然语言生成</li> <li>预测性维护系统</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/hidden-markov-model-%E9%9A%90%E9%A9%AC%E5%B0%94%E5%8F%AF%E5%A4%AB%E6%A8%A1%E5%9E%8B/">Hidden Markov Model (隐马尔可夫模型)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/mdp-%E9%A9%AC%E5%B0%94%E5%8F%AF%E5%A4%AB%E5%86%B3%E7%AD%96%E8%BF%87%E7%A8%8B/">MDP (马尔可夫决策过程)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/bayesian-network-%E8%B4%9D%E5%8F%B6%E6%96%AF%E7%BD%91%E7%BB%9C/">Bayesian Network (贝叶斯网络)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/transition-matrix-%E8%BD%AC%E7%A7%BB%E7%9F%A9%E9%98%B5/">Transition Matrix (转移矩阵)</a></li> </ul>高斯https://terms-en.ai-term-hub.com/zh/terms/gaussian/Sat, 18 Jul 2026 10:51:30 +0000https://terms-en.ai-term-hub.com/zh/terms/gaussian/<h2 id="definition">Definition</h2> <p>高斯指的是正态分布，这是一种由均值和方差表征的连续概率分布。在AI中，它广泛用于概率建模、贝叶斯推断以及</p> <h3 id="summary">Summary</h3> <p>与正态分布相关，这是一种钟形曲线，是统计学和AI噪声建模的基础。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>正态分布</li> <li>均值</li> <li>方差</li> <li>概率密度</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>噪声建模</li> <li>贝叶斯网络</li> <li>权重初始化</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> numpy <span style="color:#66d9ef">as</span> np </span></span><span style="display:flex;"><span><span style="color:#75715e"># Generate 1000 samples from a standard Gaussian distribution</span> </span></span><span style="display:flex;"><span>samples <span style="color:#f92672">=</span> np<span style="color:#f92672">.</span>random<span style="color:#f92672">.</span>normal(loc<span style="color:#f92672">=</span><span style="color:#ae81ff">0.0</span>, scale<span style="color:#f92672">=</span><span style="color:#ae81ff">1.0</span>, size<span style="color:#f92672">=</span><span style="color:#ae81ff">1000</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/distribution-%E5%88%86%E5%B8%83/">Distribution (分布)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/bell-curve-%E9%92%9F%E5%BD%A2%E6%9B%B2%E7%BA%BF/">Bell Curve (钟形曲线)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/standard-deviation-%E6%A0%87%E5%87%86%E5%B7%AE/">Standard Deviation (标准差)</a></li> </ul>贝叶斯https://terms-en.ai-term-hub.com/zh/terms/bayesian/Sat, 18 Jul 2026 10:49:18 +0000https://terms-en.ai-term-hub.com/zh/terms/bayesian/<h2 id="definition">Definition</h2> <p>AI中的贝叶斯方法利用概率论，随着更多证据的出现来更新假设的可能性。这种方法允许模型量化不确定性并动态优化预测结果。</p> <h3 id="summary">Summary</h3> <p>指基于贝叶斯定理的统计方法，用于根据新证据更新概率。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>贝叶斯定理</li> <li>先验概率</li> <li>后验概率</li> <li>不确定性量化</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>垃圾邮件过滤</li> <li>医疗诊断系统</li> <li>A/B测试分析</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">from</span> sklearn.naive_bayes <span style="color:#f92672">import</span> GaussianNB </span></span><span style="display:flex;"><span>model <span style="color:#f92672">=</span> GaussianNB() </span></span><span style="display:flex;"><span>model<span style="color:#f92672">.</span>fit(X_train, y_train) </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/probability-%E6%A6%82%E7%8E%87/">Probability (概率)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/naive-bayes-%E6%9C%B4%E7%B4%A0%E8%B4%9D%E5%8F%B6%E6%96%AF/">Naive Bayes (朴素贝叶斯)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/inference-%E6%8E%A8%E6%96%AD/">Inference (推断)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/statistics-%E7%BB%9F%E8%AE%A1%E5%AD%A6/">Statistics (统计学)</a></li> </ul>