基于对模糊过渡的观察,对部分可访问的马尔科夫网络进行产生的估计
1II. Institut für Theoretische Physik, Universität Stuttgart, 70550 Stuttgart, Germany.
Physical review. E
|June 22, 2024
概括
在部分可观测的马尔科夫网络中估计产量是具有挑战性的. 本研究引入了一种新的,非马科夫式的描述和一个可访问的估计器,通过数值示例验证.
科学领域:
- 随机热力学 随机热力学 随机热力学
- 信息理论 信息理论
- 网络分析 网络分析
背景情况:
- 估计产量在随机热力学中至关重要,特别是在部分可访问的马尔科夫网络中.
- 观察无法区分的过渡使外部观察者对网络动态产生模糊.
- 现有的模型经常假设完全解决过渡,这可能不适用于现实世界的场景.
研究的目的:
- 为部分可访问的马尔科夫网络开发一种有效的,基于过渡的描述.
- 为模糊过渡场景推导一个可操作的估计器.
- 评估拟议的估计器的实际相关性和准确性.
主要方法:
- 为模糊过渡的网络制定有效的,非马科夫式的描述.
- 使用信息理论来构建估计器.
- 对各种网络示例进行数值分析,以验证结果.
主要成果:
- 部分可访问网络的有效描述通常是非马科维的.
- 一个操作上可访问的估计器被成功导出.
- 数字模拟证实了估计器的相关性和质量.
结论:
- 开发的框架提供了一种可靠的方法,用于在部分可观测系统中估计.
- 非马科夫的描述准确地捕捉了模糊过渡的动态.
- 这项工作为推进随机热力学研究提供了有价值的工具.
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