在贝叶斯反向问题的高效超参数估计,使用样本平均近似值
Julianne Chung1, Scot M Miller2, Malena Sabate Landman3
1Department of Mathematics, Emory University, Atlanta, GA, USA.
概括
本研究介绍了使用随机平均近似和预条件兰佐斯方法估计贝叶斯反向问题的超参数的高效方法. 这种方法加快了地震断层扫描的计算速度,增强了复杂模型的贝叶斯推理.
科学领域:
- 应用数学 应用数学 应用数学
- 计算地质物理学计算地质物理学
- 贝叶斯的推理 贝叶斯的推理
背景情况:
- 贝叶斯反向问题通常需要从数据中估计先验和噪声模型的超参数.
- 与高斯噪声和马特恩共变率先验的线性反向问题需要计算密集的后期最大 (MAP) 估计,涉及日志确定因素.
研究的目的:
- 开发用于贝叶斯反向问题的超参数估计的计算高效方法.
- 为了解决MAP估计中日志决定数计算的计算负担.
主要方法:
- 使用目标函数的随机平均近似 (SAA).
- 采用预先条件的兰佐斯方法来进行高效函数和梯度近似.
- 为超参数提出一种新的,便宜的可更新的预条件器.
- 通过重复使用功能评估信息来开发近似梯度评估的方法.
主要成果:
- 提出的方法显著降低了与超参数估计相关的计算成本.
- 新的预条件器允许在超参数值发生变化时进行高效的更新.
- 在梯度近似中重复使用信息进一步提高了计算效率.
结论:
- 开发的技术为复杂的贝叶斯反向问题的超参数估计提供了一个计算可行的方法.
- 这些方法在静态和动态地震断层扫描问题上得到了成功的证明,展示了它们的实际应用.
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