83 citations · 120 across the 4 of their papers we have counts for
5 papers
The True Cost of Stochastic Gradient Langevin Dynamics
Tigran Nagapetyan, Andrew B. Duncan, Leonard Hasenclever +3
The problem of posterior inference is central to Bayesian statistics and a wealth of Markov Chain Monte Carlo (MCMC) methods have been proposed to obtain asymptotically correct sam…
Multilevel Monte Carlo for Scalable Bayesian Computations
Mike Giles, Tigran Nagapetyan, Lukasz Szpruch +2
Markov chain Monte Carlo (MCMC) algorithms are ubiquitous in Bayesian computations. However, they need to access the full data set in order to evaluate the posterior density at eve…
Multilevel Monte Carlo methods for the approximation of invariant measures of stochastic differential equations
Michael B. Giles, Mateusz B. Majka, Lukasz Szpruch +2
We develop a framework that allows the use of the multi-level Monte Carlo (MLMC) methodology (Giles2015) to calculate expectations with respect to the invariant measure of an ergod…
Multilevel Monte Carlo methods for applications in finance
Mike Giles, Lukasz Szpruch
Since Giles introduced the multilevel Monte Carlo path simulation method [18], there has been rapid development of the technique for a variety of applications in computational fina…
Antithetic multilevel Monte Carlo estimation for multi-dimensional SDEs without Lévy area simulation
Michael B. Giles, Lukasz Szpruch
In this paper we introduce a new multilevel Monte Carlo (MLMC) estimator for multi-dimensional SDEs driven by Brownian motions. Giles has previously shown that if we combine a nume…