activity
20152021
most citedApproximate Bayesian computation with the Wasserstein distance

137 citations · 199 across the 7 of their papers we have counts for

collaborators

7 papers

math.OC2021★ 1 cited

Stability of Entropic Optimal Transport and Schrödinger Bridges

Promit Ghosal, Marcel Nutz, Espen Bernton

We establish the stability of solutions to the entropically regularized optimal transport problem with respect to the marginals and the cost function. The result is based on the ge…

math.OC2021★ 1 cited

Entropic Optimal Transport: Geometry and Large Deviations

Espen Bernton, Promit Ghosal, Marcel Nutz

We study the convergence of entropically regularized optimal transport to optimal transport. The main result is concerned with the convergence of the associated optimizers and take…

stat.CO2019★ 11 cited

Schrödinger Bridge Samplers

Espen Bernton, Jeremy Heng, Arnaud Doucet +1

Consider a reference Markov process with initial distribution and transition kernels , for some . Assume that you are given distribut…

stat.ME2019★ 137 cited

Approximate Bayesian computation with the Wasserstein distance

Espen Bernton, Pierre E. Jacob, Mathieu Gerber +1

A growing number of generative statistical models do not permit the numerical evaluation of their likelihood functions. Approximate Bayesian computation (ABC) has become a popular…

stat.CO2018★ 24 cited

Langevin Monte Carlo and JKO splitting

Espen Bernton

Algorithms based on discretizing Langevin diffusion are popular tools for sampling from high-dimensional distributions. We develop novel connections between such Monte Carlo algori…

stat.ME2017★ 21 cited

On parameter estimation with the Wasserstein distance

Espen Bernton, Pierre E. Jacob, Mathieu Gerber +1

Statistical inference can be performed by minimizing, over the parameter space, the Wasserstein distance between model distributions and the empirical distribution of the data. We…

stat.CO2015★ 4 cited

Locally weighted Markov chain Monte Carlo

Espen Bernton, Shihao Yang, Yang Chen +2

We propose a weighting scheme for the proposals within Markov chain Monte Carlo algorithms and show how this can improve statistical efficiency at no extra computational cost. Thes…