马科夫轨迹的随机区分能力
Asawari Pagare1, Zhongmin Zhang1, Jiming Zheng1
1Department of Chemistry, University of North Carolina, Chapel Hill, North Carolina 27514, USA.
The Journal of chemical physics
|May 15, 2024
概括
本研究引入了一种新的公式,通过分析它们的轨迹数据来衡量两个随机系统之间的差异. 这种方法克服了采样挑战,并揭示了有助于区分的关键动态特征.
科学领域:
- 热力学是一种热力学.
- 化学 化学 化学
- 生物物理学的生物物理.
- 随机过程 随机过程
背景情况:
- 在多个科学领域中,使用它们的轨迹来区分随机系统至关重要.
- 库尔巴克-莱布勒 (KL) 差异量化了轨迹的区分能力,但很难从有限的数据中评估.
- 现有的理论缺乏动态特征和轨迹区分能力之间的明确联系.
研究的目的:
- 为任何马尔科夫过程开发一种分解KL空间和时间分歧的通用公式.
- 提供一种方法,绕过与评估轨迹KL分歧相关的采样困难.
- 为了明确地将轨迹KL分歧与过渡事件和等待时间统计数据联系起来.
主要方法:
- 对KL差异分解的一般公式的推导.
- 来自马尔科夫过程的轨迹合集的分析.
- 将KL分歧与过渡事件统计和等待时间联系起来.
主要成果:
- 介绍了轨迹KL分歧的时空分解的新型公式.
- 该方法有效地规避了评估区分能力的采样限制.
- 在KL分歧,过渡事件和等待时间统计数据之间建立了明确的联系.
结论:
- 开发的公式提供了一种新的方法来理解和量化马尔科夫过程之间的区分能力.
- 这项工作为管理系统区分能力的动态特征提供了洞察力.
- 这些发现为生物传感器设计和信号传导优化中的新理论框架铺平了道路.
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