Reflection couplings and contraction rates for diffusions
TLDR
In this paper, the authors consider contractivity for diffusion semigroups w.r.t. Kantorovich distances based on appropriately chosen concave functions and show that with appropriate explicit choices of the underlying distance, contractivity with rates of close to optimal order can be obtained in several fundamental classes of examples.Abstract:
We consider contractivity for diffusion semigroups w.r.t. Kantorovich ($L^1$ Wasserstein) distances based on appropriately chosen concave functions. These distances are inbetween total variation and usual Wasserstein distances. It is shown that by appropriate explicit choices of the underlying distance, contractivity with rates of close to optimal order can be obtained in several fundamental classes of examples where contractivity w.r.t. standard Wasserstein distances fails. Applications include overdamped Langevin diffusions with locally non-convex potentials, products of these processes, and systems of weakly interacting diffusions, both of mean-field and nearest neighbour type.read more
Citations
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Journal ArticleDOI
Nonasymptotic convergence analysis for the unadjusted Langevin algorithm
Alain Durmus,Eric Moulines +1 more
TL;DR: In this article, a sampling technique based on the Euler discretization of the Langevin stochastic differential equation is studied, and for both constant and decreasing step sizes, non-asymptotic bounds for the convergence to stationarity in both total variation and Wasserstein distances are obtained.
Journal ArticleDOI
Efficient Bayesian computation by proximal Markov chain Monte Carlo: when Langevin meets Moreau
TL;DR: In this paper, a Markov chain Monte Carlo (MCMC) method is proposed for high-dimensional models that are log-concave and nonsmooth, a class of models that is central in imaging sciences.
Posted Content
Sharp Convergence Rates for Langevin Dynamics in the Nonconvex Setting.
TL;DR: Both overdamped and underdamped Langevin MCMC are studied and upper bounds on the number of steps required to obtain a sample from a distribution that is within $\epsilon$ of $p*$ in $1$-Wasserstein distance are established.
Supplement to "High-dimensional Bayesian inference via the Unadjusted Langevin Algorithm"
Alain Durmus,Eric Moulines +1 more
TL;DR: In this article, the authors proposed a method to solve the problem of the "missing link" problem in the context of biomedical data.http://hal.archives-ouvertes.fr/hal-01304430
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Rapid Mixing of Hamiltonian Monte Carlo on Strongly Log-Concave Distributions
Oren Mangoubi,Aaron Smith +1 more
TL;DR: In this paper, the mixing properties of the Hamiltonian Monte Carlo (HMC) algorithm for a strongly log-concave target distribution were studied and it was shown that HMC mixes quickly in this setting.
References
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Book
Lectures on the Coupling Method
TL;DR: In this article, the authors propose Discrete Theory Continuous Theory, Inequalities Intensity-Governed Processes Diffusions Appendix Frequently Used Notation References Index, Section 5.
Book
Analysis and Geometry of Markov Diffusion Operators
TL;DR: Semigroups of bounded operators on a Banach space have been studied in this paper for Riemannian geometry and Markov semigroups have been used for stochastic calculus.
Journal ArticleDOI
Stochastic differential equations with reflecting boundary conditions
TL;DR: On resout des equations differentielles stochastiques a conditions aux limites reflechissantes par une approche directe basee sur le probleme de Skorokhod as discussed by the authors.
Book
Functional Integration And Partial Differential Equations
TL;DR: The authors discusses some aspects of the theory of partial differential equations from the viewpoint of probability theory and provides results that have not previously appeared in book form, including research contributions of the author.
Book
Stochastic analysis on manifolds
TL;DR: Brownian motion and analytic index theorems analysis on path spaces are studied in this article for stochastic differential equations and diffusions in the context of path spaces, where the heat kernel is used as an index.
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