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arXiv 2102.13040math.PRq-bio.MN
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速率一致递减马氏跳过程的大偏差

Large deviations for Markov jump processes with uniformly diminishing rates

Andrea Agazzi, Luisa Andreis, Robert I. A. Patterson, D. R. Michiel Renger

摘要:

本文证明了小噪声限制下跳马尔可夫过程样本路径的大偏差原理(LDP),当所有跳率在状态空间的一个区域内均匀但足够慢地消失时,本文证明了该大偏差原理.我们进一步讨论了我们的假设的最优性的衰减的跳跃率。作为这项工作的直接应用,我们放松了应用LDPs所需的假设,例如,化学反应网络动力学,其中消失的反应速率自然出现,特别是质量作用动力学的背景下。

英文摘要:

We prove a large-deviation principle (LDP) for the sample paths of jump Markov processes in the small noise limit when, possibly, all the jump rates vanish uniformly, but slowly enough, in a region of the state space. We further discuss the optimality of our assumptions on the decay of the jump rates. As a direct application of this work we relax the assumptions needed for the application of LDPs to, e.g., Chemical Reaction Network dynamics, where vanishing reaction rates arise naturally particularly the context of mass action kinetics.

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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