在到达概率方面,马尔科夫跳跃过程的稳定状态解决方案
1University of Padova, Department of Chemical Sciences, via Marzolo 1, I-35131 Padova, Italy.
Physical review. E
|February 20, 2025
概括
这项研究使用马尔科夫跳跃过程来模拟动态过程. 它发现到达和返回概率高达N-1跳跃是确定复杂网络中稳定状态分布的关键.
科学领域:
- 物理化学 物理化学
- 化学动力学 化学动力学
- 统计力学 统计力学
背景情况:
- 动态过程通常被建模为跨越离散状态的马尔科夫跳跃过程.
- 了解稳定状态占用概率对于分析这些系统至关重要.
- 以前的模型往往侧重于时间依赖的方面,忽视了基于跳跃的概率.
研究的目的:
- 开发一个物理框架的表达式,用于稳态分布在紧密相连的网络.
- 定义和利用基于跳跃数量的到达概率,独立于时间.
- 用回报概率来确定稳定状态分布的必要和充分条件.
主要方法:
- 建模系统作为马尔科夫跳跃过程,具有N个位点和时间独立的跳跃率.
- 将到达概率定义为在特定数量的跳跃内在站点之间过渡的概率.
- 使用完整的返回概率集合,直到N-1跳跃.
主要成果:
- 用到达概率来导出稳态分布的一个新表达式.
- 这项研究表明,高达N-1个跳跃的回归概率是必要的,也是足够的,以表征稳定状态.
- 该框架以示例说明,包括随机化学动力学.
结论:
- 拟议的方法为分析复杂动态系统的稳定状态属性提供了一个强大的框架.
- 抵达和返回概率为系统动态提供了一个强大的,独立于时间的视角.
- 这种方法适用于各种领域,包括化学动力学和网络分析.
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