从无穷大下降：尾分布的收敛性
Descending from infinity: Convergence of tailed distributions
摘要：
我们调查放松长尾分布的随机动力学不支持这样的尾巴。线性松弛被发现是一个边界的情况下，长尾指数抑制的时间，但没有消除。比线性更强的松弛立即抑制长尾，但可能导致概率分布中的强瞬态峰值。一个δ函数的初始分布下，强于线性衰减显示不是一个，而是两个不同的制度的扩散蔓延。
英文摘要：
We investigate the relaxation of long-tailed distributions under stochastic dynamics that do not support such tails. Linear relaxation is found to be a borderline case in which long tails are exponentially suppressed in time but not eliminated. Relaxation stronger than linear suppresses long tails immediately, but may lead to strong transient peaks in the probability distribution. A delta function initial distribution under stronger than linear decay displays not one but two different regimes of diffusive spreading.
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。
