47 lines
1.5 KiB
Markdown
47 lines
1.5 KiB
Markdown
---
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title: "精度加权融合 (Precision-Weighted Fusion)"
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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: ["multimodal-learning", "bayesian-deep-learning", "fusion"]
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sources: ["[[principled-uncertainty-clinical-ai]]"]
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---
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# 精度加权融合 (Precision-Weighted Fusion)
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**精度加权融合**是 [[principled-uncertainty-clinical-ai|Principled Uncertainty in Clinical AI]] 提出的多模态贝叶斯融合机制，利用各模态潜空间方差的倒数作为精度权重进行分布组合。
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## 核心机制
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对于 M 个可用模态，每个编码器输出 (mu_m, log sigma^2_m)：
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**步骤 1**：计算各模态精度
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```
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Lambda_m = exp(-log sigma^2_m) = 1 / sigma^2_m
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```
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**步骤 2**：精度加权组合
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```
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Lambda_fused = sum_m Lambda_m
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sigma^2_fused = 1 / Lambda_fused
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mu_fused = (sum_m Lambda_m * mu_m) / Lambda_fused
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```
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## 缺失模态处理
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缺失模态通过设置 log sigma^2 = 10.0 来处理：
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- Lambda ≈ 0（精度近似为零）
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- 该模态自动从融合中排除
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- 保留患者记录完整性
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结果：融合不确定性始终 ≥ 最小单模态方差，随着缺失模态增多而正确增加。
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## 直觉
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高精度（低方差）的模态贡献更多权重——就像贝叶斯推断中精度加权的后验组合。这是卡尔曼滤波思想在多模态融合中的推广。
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## 参考
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- [[principled-uncertainty-clinical-ai|Principled Uncertainty in Clinical AI]]
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- [[epistemic-uncertainty|认知不确定性]]
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- [[uncertainty-quantification|不确定性量化]]
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