Volume 26, 2022
|Page(s)||69 - 125|
|Published online||31 January 2022|
Approximating quasi-stationary distributions with interacting reinforced random walks
Department of Statistics and Operations Research, University of North Carolina at Chapel Hill.
* Corresponding author: email@example.com
Accepted: 17 December 2021
We propose two numerical schemes for approximating quasi-stationary distributions (QSD) of finite state Markov chains with absorbing states. Both schemes are described in terms of certain interacting chains in which the interaction is given in terms of the total time occupation measure of all particles in the system and has the impact of reinforcing transitions, in an appropriate fashion, to states where the collection of particles has spent more time. The schemes can be viewed as combining the key features of the two basic simulation-based methods for approximating QSD originating from the works of Fleming and Viot (1979) and Aldous, Flannery and Palacios (1998), respectively. The key difference between the two schemes studied here is that in the first method one starts with a(n) particles at time 0 and number of particles stays constant over time whereas in the second method we start with one particle and at most one particle is added at each time instant in such a manner that there are a(n) particles at time n. We prove almost sure convergence to the unique QSD and establish Central Limit Theorems for the two schemes under the key assumption that a(n) = o(n). When a(n) ~ n, the fluctuation behavior is expected to be non-standard. Some exploratory numerical results are presented to illustrate the performance of the two approximation schemes.
Mathematics Subject Classification: 60J10 / 34F05 / 60F10 / 92D25
Key words: Quasi-stationary distributions / stochastic approximation / interacting particles / central limit theorem / reinforced random walks / self-interaction / Fleming-Viot particle approximations
© The authors. Published by EDP Sciences, SMAI 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.