Approximation for the invariant measure with applications for jump processes (convergence in total variation distance)

In this paper, we establish an abstract framework for the approximation of the invariant probability measure for a Markov semigroup. Following Pagès and Panloup (2022) we use an Euler scheme with decreasing step (unadjusted Langevin algorithm). Under some contraction property with exponential rate a...

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Bibliographic Details
Published in:Stochastic processes and their applications 2024-10, Vol.176, p.104416, Article 104416
Main Authors: Bally, Vlad, Qin, Yifeng
Format: Article
Language:English
Subjects:
Euler scheme with decreasing steps
Invariant measure
Jump process
Malliavin calculus
Regularization lemma
Total variation distance
Unadjusted Langevin algorithm
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Summary:In this paper, we establish an abstract framework for the approximation of the invariant probability measure for a Markov semigroup. Following Pagès and Panloup (2022) we use an Euler scheme with decreasing step (unadjusted Langevin algorithm). Under some contraction property with exponential rate and some regularization properties, we give an estimate of the error in total variation distance. This abstract framework covers the main results in Pagès and Panloup (2022) and Chen et al. (2023). As a specific application we study the convergence in total variation distance to the invariant measure for jump type equations. The main technical difficulty consists in proving the regularization properties — this is done under an ellipticity condition, using Malliavin calculus for jump processes.
ISSN:0304-4149
DOI:10.1016/j.spa.2024.104416