一般化的贝叶斯方法用于与模型错误规范的反向问题
Youngsoo Baek1, Wilkins Aquino2, Sayan Mukherjee1,3,4,5
1Department of Statistical Science, Duke University, Durham, NC, United States of America.
概括
我们引入了一个新的概率框架,用于解决基于偏微分方程 (PDE) 的反向问题,而不需要假设概率模型. 这种方法增强了不确定性量化和复杂应用的模型选择.
科学领域:
- 计算数学 计算数学 计算数学
- 应用数学 应用数学 应用数学
- 科学计算科学计算
背景情况:
- 贝叶斯方法是反向问题的不确定性量化标准.
- 它们需要准确的概率模型,这些模型通常是不可用的或难以指定的.
- 这限制了它们在现实场景中的应用,因为数据生成过程很复杂.
研究的目的:
- 为基于PDE的反向问题开发概率解决方案的一般框架.
- 解决贝叶斯推理中未知概率模型的挑战.
- 引入基于预测性能的新型模型比较框架.
主要方法:
- 使用吉布斯后方框架,在概率分布的空间上解决正则化的变量问题.
- 开发一个模型比较框架,通过预测性能来评估损失函数的最佳性.
- 实施调整参数校准和损失函数比较的交叉验证.
主要成果:
- 证明了基于PDE的反向问题的新型概率框架.
- 介绍了吉布斯后部的理论性质.
- 用超声波振动计模拟示例验证了框架,用于动脉血管表征.
结论:
- 拟议的框架为在不确定性模型未知时的不确定性量化提供了一个强大的替代方案.
- 模型比较方法有助于选择最佳的损失函数.
- 该方法在医学成像和其他领域的应用方面表现有前途.
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