We study a stochastic process defined by the interaction strength for the return to the mean and a stochastic term proportional to the magnitude of the variable. Its steady-state distribution is the Inverse Gamma distribution, whose power-law tail exponent is determined by the ratio of the interaction strength to stochasticity. Its time-dependence is characterized by a set of discrete times describing relaxation of respective cumulants to their steady-state values. We show that as the progressively lower cumulants diverge with the increase of stochasticity, so do their relaxation times. We analytically evaluate the correlation function and show that it is determined by the longest of these times, namely the inverse interaction strength, which is also the relaxation time of the mean. We also investigate relaxation of the entire distribution to the steady state and the distribution of relaxations times, which we argue to be Inverse Gaussian.
University of Luxembourg（卢森堡大学）；Center for Theoretical Physics of Complex Systems, Institute for Basic Science(IBS)（韩国基础科学研究院复杂系统理论物理中心）；Universidad del Valle（德瓦列大学）；ICTP South American Institute for Fundamental Research（国际理论物理研究所南美基础研究中心）；Instituto de Física Teórica, UNESP - Univ. Estadual Paulista（圣保罗州立大学理论物理研究所）；Center for Trapped Ions Quantum Science, Institute for Basic Science（韩国基础科学研究院捕获离子量子科学中心）；Basic Science Program, Korea University of Science and Technology (UST)（韩国科学技术院基础科学项目）
