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2016 Approximation of Markov semigroups in total variation distance
Vlad Bally, Clément Rey
Electron. J. Probab. 21: 1-44 (2016). DOI: 10.1214/16-EJP4079


In this paper, we consider Markov chains of the form $X^{n}_{(k+1)/n}=\psi _{k}(X^{n}_{k/n},Z_{k+1}/\sqrt{n},1/n)$ where the innovation comes from the sequence $Z_{k},k\in \mathbb{N} ^{\ast }$ of independent centered random variables with arbitrary law. Then, we study the convergence $\mathbb{E} [f(X^{n}_t)]\rightarrow \mathbb{E} [f(X_t)]$ where $(X_t)_{t \geqslant 0}$ is a Markov process in continuous time. This may be considered as an invariance principle, which generalizes the classical Central Limit Theorem to Markov chains. Alternatively (and this is the main motivation of our paper), $X^{n}$ may be an approximation scheme used in order to compute $\mathbb{E} [f(X_t)]$ by Monte Carlo methods. Estimates of the error are given for smooth test functions $f$ as well as for measurable and bounded $f.$ In order to prove convergence for measurable test functions we assume that $Z_{k}$ satisfies Doeblin’s condition and we use Malliavin calculus type integration by parts formulas based on the smooth part of the law of $Z_{k}$. As an application, we will give estimates of the error in total variation distance for the Ninomiya Victoir scheme.


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Vlad Bally. Clément Rey. "Approximation of Markov semigroups in total variation distance." Electron. J. Probab. 21 1 - 44, 2016.


Received: 27 January 2015; Accepted: 27 December 2015; Published: 2016
First available in Project Euclid: 17 February 2016

zbMATH: 1338.60097
MathSciNet: MR3485354
Digital Object Identifier: 10.1214/16-EJP4079

Primary: 60F17, 60H07, 65C40


Vol.21 • 2016
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