贝叶斯机械推理,统计力学和蒙特卡洛的新时代
Daniel M Zuckerman1, August George1
1Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon 97239, United States.
Journal of chemical theory and computation
|April 11, 2024
概括
贝叶斯推理 (BI) 提供了一种复杂的"自上而下的"计算方法,用于模拟物理科学中观察到的数据. 本综述将BI与统计力学联系起来,强调其在通过蒙特卡洛算法进行参数估计和模型选择中的使用.
科学领域:
- 计算化学计算化学
- 统计力学 统计力学
- 贝叶斯的推理是贝叶斯的推理.
背景情况:
- 传统的计算化学使用来自物理定律的"自下而上的"方法.
- 最近"自上而下的"计算的进步利用贝叶斯推理 (BI) 来进行数据驱动的建模.
- BI估计参数,不确定性和相关性,有助于模型选择.
研究的目的:
- 为物理科学家提供贝叶斯推理 (BI) 的教学介绍.
- 通过物理镜头审查BI的关键方面,将其与统计力学联系起来.
- 描述BI的有希望的抽样算法,特别是蒙特卡洛 (MC) 方法.
主要方法:
- 制定与观察数据一致的数学模型.
- 使用贝叶斯推理进行参数估计和模型选择.
- 根据统计力学原理改进和开发蒙特卡洛 (MC) 算法.
主要成果:
- BI可以估计参数值,不确定性和相关性.
- BI 便于模型选择,区分不同的模型结构.
- 某些MC算法,特别是那些使用"无限温度"参考状态的算法,在特定参数范围内对BI有效.
结论:
- 贝叶斯推理为"自下而上的"计算化学提供了一个强大的"自上而下的"补充.
- 统计力学的框架为BI提供了有价值的见解,包括能源景观和自由能源计算.
- 了解BI,MC算法和统计力学之间的相互作用对于推进计算建模至关重要.
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