40 lines
1.6 KiB
Markdown
40 lines
1.6 KiB
Markdown
---
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title: "贝叶斯深度学习 (Bayesian Deep Learning)"
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created: 2026-06-10
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updated: 2026-06-10
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type: concept
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tags: ["uncertainty-quantification", "variational-inference"]
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sources: ["[[principled-uncertainty-clinical-ai]]"]
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---
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# 贝叶斯深度学习 (Bayesian Deep Learning)
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**贝叶斯深度学习**将贝叶斯推断框架应用于深度神经网络，为权重和预测赋予概率分布而非点估计，从而自然地实现 [[uncertainty-quantification|不确定性量化]]。
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## 核心思想
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传统深度学习：学习确定性的权重 w -> 输出点估计 y_hat = f_w(x)
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贝叶斯深度学习：学习权重的后验分布 p(w|D) -> 输出预测分布 p(y|x, D)
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## 变分推断方法
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由于精确后验不可计算，使用变分推断近似：
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1. **[[variational-autoencoder|变分自编码器]]**（Kingma & Welling, 2014）：通过重参数化技巧优化 ELBO
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2. **[[mc-dropout|MC Dropout]]**（Gal & Ghahramani, 2016）：训练时 Dropout ≈ 深度高斯过程中的贝叶斯推断
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3. **Bayes by Backprop**（Blundell et al., 2015）：直接在权重上学习分布
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## 临床 AI 应用
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[[principled-uncertainty-clinical-ai|Principled Uncertainty in Clinical AI]] 展示了完整的端到端贝叶斯管线：
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- 模态特定变分编码器 → 潜空间分布
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- [[precision-weighted-fusion|精度加权融合]] → 融合后验
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- 分解不确定性头 → [[epistemic-uncertainty|认知]] + [[aleatoric-uncertainty|随机]]
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## 参考
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- [[principled-uncertainty-clinical-ai|Principled Uncertainty in Clinical AI]]
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- [[variational-autoencoder|VAE]]
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- [[mc-dropout|MC Dropout]]
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