Markov Chains and Monte Carlo Methods
Open access
Peer reviewed
Abstract
Abstract
Abstract We extend the Lévy Langevin Monte Carlo method studied by Oechsler (2024): Choosing a heavy-tailed target distribution we prove convergence of a solution of a stochastic differential equation to this target. Hereby, the stochastic differential equation is driven by a compound Poisson process - unlike in the case of a classical Langevin diffusion. The method allows one to sample from non-smooth targets and distributions with separated modes with exponential convergence to the invariant distribution, which in general cannot be guaranteed by the classical Langevin diffusion in presence of heavy tails. The method is promising due to the possibility of a simple implementation because of the compound Poisson noise term.
Direct answer
What can I do from this paper page?
Use this page to scan "Lévy Langevin Monte Carlo for sampling from heavy-tailed target distributions" quickly: start with the summary and abstract, then check the authors, source, topics, and related papers. From here, open Scollr to follow Markov Chains and Monte Carlo Methods research, save the paper, or map adjacent work.
Research areas
Follow related topics
Citation
BibTeX
@article{Behme2026Langevin,
title = {Lévy Langevin Monte Carlo for sampling from heavy-tailed target distributions},
author = {Anita Behme and Claudius Lütke Schwienhorst},
journal = {Extremes},
year = {2026},
doi = {10.1007/s10687-026-00544-9},
url = {https://doi.org/10.1007/s10687-026-00544-9}
}FAQ
Using this paper in a discovery workflow
How do I find related work for this paper?
Use the related papers and topic links on this page as starting points. In Scollr, you can also open the paper and build a literature map around its references, citing papers, and related work.
How can I keep up with new Markov Chains and Monte Carlo Methods research papers?
Follow Markov Chains and Monte Carlo Methods research in Scollr. New papers from the topic flow into a personalized feed, and you can save useful studies to revisit later.
Can I cite this paper from this page?
This page includes a static BibTeX block for Lévy Langevin Monte Carlo for sampling from heavy-tailed target distributions. Always verify the DOI, source, and publication details against the publisher record before submitting a manuscript.
Follow this research in Scollr
Follow the topics and authors behind this paper, save useful studies, and build a literature map when you are ready to go deeper.
Get the app