A convergent numerical algorithm for the stochastic growth-fragmentation problem

Abstract

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.

References in corpus (3)

• Renewal theory and computable convergence rates for geometrically ergodic Markov chains
• On the Inverse Problem for a Size-Structured Population Model
• Cyclic asymptotic behaviour of a population reproducing by fission into two equal parts