巨大的跳跃和长距离的漂移：具有长程记忆的系统中的涨落机制
Giant leaps and long excursions: fluctuation mechanisms in systems with long-range memory
摘要：
本文分析了具有长程记忆的随机过程中时均量的大偏差，其中t时刻的动态依赖于时均量的q_t值。首先，我们考虑大象随机游走和高斯变量的这个模型，确定两个机制不寻常的波动行为，这不同于马尔可夫的情况下。特别是，内存可以导致大偏差的原则与降低的速度，和非解析率函数。然后，我们解释如何在这两个模型中运行的机制是通用的记忆依赖的动态，并显示其他例子，包括非马尔可夫对称排斥过程。
英文摘要：
We analyse large deviations of time-averaged quantities in stochastic processes with long-range memory, where the dynamics at time t depends itself on the value q_t of the time-averaged quantity. First we consider the elephant random walk and a Gaussian variant of this model, identifying two mechanisms for unusual fluctuation behaviour, which differ from the Markovian case. In particular, the memory can lead to large deviation principles with reduced speeds, and to non-analytic rate functions. We then explain how the mechanisms operating in these two models are generic for memory-dependent dynamics and show other examples including a non-Markovian symmetric exclusion process.
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。
