1. Denis Belomestny, Stefan Häfner, and Mikhail Urusov. Variance reduction for discretised diffusions via regression. Journal of Mathematical Analysis and Applications, 458:393—418, 2018.
2. Robert Christian and George Casella. Monte carlo statistical methods, 1999.
3. Stephan Clemencon, Gabor Lugosi, and Nicolas Vayatis. Ranking and empirical minimization of u-statistics. Ann. Statist., 36(2):844—874, 04 2008.
4. Ivan T. Dimov. Monte Carlo methods for applied scientists. World Scientifc, 2008.
5. Paul Glasserman. Monte Carlo methods in fnancial engineering, volume 53. Springer Science & Business Media, 2013.
6. Wassily Hoeffding. Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association, 58(301):13—30, 1963.
7. Richard Nickl and Benedikt M. Pötscher. Bracketing metric entropy rates and empirical central limit theorems for function classes of besov-and sobolev-type. Journal of Theoretical Probability, 20(2):177—199, 2007.
8. Chris J. Oates, Jon Cockayne, François-Xavier Briol, and Mark Girolami. Convergence rates for a class of estimators based on steinTs identity. arXiv preprint arXiv:1603.03220, 2016.
9. Chris J. Oates, Mark Girolami, and Nicolas Chopin. Control functionals for monte carlo integration. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 79(3):695—718, 2017.
10. Reuven Y. Rubinstein and Dirk P. Kroese. Simulation and the Monte Carlo method, volume 10. John Wiley & Sons, 2016.