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arXiv 2309.06970math.PR
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一种用于判断 $\mathbb Z^d$ 上 Markov 过程指数遍历性的新路径方法及其在随机反应网络中的应用

A new path method for exponential ergodicity of Markov processes on $\mathbb Z^d$, with applications to stochastic reaction networks

David F. Anderson, Daniele Cappelletti, Wai-Tong Louis Fan, Jinsu Kim

AI总结:

本文提出一种无需时间可逆性假设的新路径方法来判定 $\mathbb Z^d$ 上连续时间 Markov 链的 $L^2$ 指数收敛,并用于证明开放复平衡随机反应网络具有正谱隙,同时构造了非指数遍历的细致平衡反例。

摘要:

本文提出了一种新的路径方法,可用于判断 $\mathbb Z^d$ 上的遍历连续时间 Markov 链何时在 $L^2$ 中以指数速度收敛到其平稳分布。具体而言,我们给出了保证谱隙为正的一般条件。重要的是,我们的结果不要求 Markov 模型具有时间可逆性这一假设。随后,我们将这一新方法应用于已被广泛研究的随机建模反应网络类别。值得注意的是,我们证明了每个同时为“开放”的 complex-balanced(复平衡)模型都具有正谱隙,因此是指数遍历的。我们进一步说明了如何将结果应用于不一定复平衡的模型。此外,我们给出了一个 detailed-balanced(在反应网络理论意义下细致平衡)因而也是复平衡的随机反应网络示例,但该网络并非指数遍历。我们认为这是文献中首个此类示例。

英文摘要:

This paper provides a new path method that can be used to determine when an ergodic continuous-time Markov chain on $\mathbb Z^d$ converges exponentially fast to its stationary distribution in $L^2$. Specifically, we provide general conditions that guarantee the positivity of the spectral gap. Importantly, our results do not require the assumption of time-reversibility of the Markov model. We then apply our new method to the well-studied class of stochastically modeled reaction networks. Notably, we show that each complex-balanced model that is also ``open'' has a positive spectral gap, and is therefore exponentially ergodic. We further illustrate how our results can be applied for models that are not necessarily complex-balanced. Moreover, we provide an example of a detailed-balanced (in the sense of reaction network theory), and hence complex-balanced, stochastic reaction network that is not exponentially ergodic. We believe this to be the first such example in the literature.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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