One-dimensional infinite memory imitation models with noise
Probability
2015-08-05 v1
Abstract
In this paper we study stochastic process indexed by constructed from certain transition kernels depending on the whole past. These kernels prescribe that, at any time, the current state is selected by looking only at a previous random instant. We characterize uniqueness in terms of simple concepts concerning families of stochastic matrices, generalizing the results previously obtained in De Santis and Piccioni (J. Stat. Phys., 150(6):1017--1029, 2013).
Cite
@article{arxiv.1508.00867,
title = {One-dimensional infinite memory imitation models with noise},
author = {Emilio De Santis and Mauro Piccioni},
journal= {arXiv preprint arXiv:1508.00867},
year = {2015}
}
Comments
22 pages, 3 figures