no code implementations • 13 Feb 2024 • Daniel Paulin, Peter A. Whalley
A method for analyzing non-asymptotic guarantees of numerical discretizations of ergodic SDEs in Wasserstein-2 distance is presented by Sanz-Serna and Zygalakis in ``Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations".
no code implementations • 8 Nov 2023 • Neil K. Chada, Benedict Leimkuhler, Daniel Paulin, Peter A. Whalley
We exhibit similar bounds using both approximate and stochastic gradients, and our method's computational cost is shown to scale logarithmically with the size of the dataset.
no code implementations • 1 Dec 2021 • EL Mahdi Khribch, George Deligiannidis, Daniel Paulin
In this paper, we consider sampling from a class of distributions with thin tails supported on $\mathbb{R}^d$ and make two primary contributions.
no code implementations • 23 May 2019 • Maxime Vono, Daniel Paulin, Arnaud Doucet
Performing exact Bayesian inference for complex models is computationally intractable.
4 code implementations • 13 Sep 2018 • Chris J. Maddison, Daniel Paulin, Yee Whye Teh, Brendan O'Donoghue, Arnaud Doucet
Yet, crucially the kinetic gradient map can be designed to incorporate information about the convex conjugate in a fashion that allows for linear convergence on convex functions that may be non-smooth or non-strongly convex.