@Article{AAM-40-1,
author = {Wu, Dawei and Zhou, Zhennan},
title = {A Convergent Numerical Algorithm for the Stochastic Growth-Fragmentation Problem},
journal = {Annals of Applied Mathematics},
year = {2024},
volume = {40},
number = {1},
pages = {71--104},
abstract = {<p style="text-align: justify;">The stochastic growth-fragmentation model describes the temporal evolution of a structured cell population through a discrete-time and
continuous-state Markov chain. The simulations of this stochastic process and
its invariant measure are of interest. In this paper, we propose a numerical
scheme for both the simulation of the process and the computation of the invariant measure, and show that under appropriate assumptions, the numerical
chain converges to the continuous growth-fragmentation chain with an explicit
error bound. With a triangle inequality argument, we are also able to quantitatively estimate the distance between the invariant measures of these two
Markov chains.</p>},
issn = {},
doi = {https://doi.org/10.4208/aam.OA-2023-0035},
url = {https://global-sci.com/article/90856/a-convergent-numerical-algorithm-for-the-stochastic-growth-fragmentation-problem}
}