• xmlui.mirage2.page-structure.header.title
    • français
    • English
  • Help
  • Login
  • Language 
    • Français
    • English
View Item 
  •   BIRD Home
  • CEREMADE (UMR CNRS 7534)
  • CEREMADE : Publications
  • View Item
  •   BIRD Home
  • CEREMADE (UMR CNRS 7534)
  • CEREMADE : Publications
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Browse

BIRDResearch centres & CollectionsBy Issue DateAuthorsTitlesTypeThis CollectionBy Issue DateAuthorsTitlesType

My Account

LoginRegister

Statistics

Most Popular ItemsStatistics by CountryMost Popular Authors
Thumbnail

Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale

Wu, Changye; Stoehr, Julien; Robert, Christian P. (2019), Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale. https://basepub.dauphine.fr/handle/123456789/18740

View/Open
1810.04449.pdf (249.3Kb)
Type
Document de travail / Working paper
External document link
https://hal.archives-ouvertes.fr/hal-01968795
Date
2019
Series title
Cahier de recherche CEREMADE, Université Paris-Dauphine
Published in
Paris
Pages
18
Metadata
Show full item record
Author(s)
Wu, Changye

Stoehr, Julien cc

Robert, Christian P.
Abstract (EN)
Hamiltonian Monte Carlo samplers have become standard algorithms for MCMC implementations, as opposed to more basic versions, but they still require some amount of tuning and calibration. Exploiting the U-turn criterion of the NUTS algorithm (Hoffman and Gelman, 2014), we propose a version of HMC that relies on the distribution of the integration time of the associated leapfrog integrator. Using in addition the primal-dual averaging method for tuning the step size of the integrator, we achieve an essentially calibration free version of HMC. When compared with the original NUTS on several benchmarks, this algorithm exhibits a significantly improved efficiency.
Subjects / Keywords
Acceleration methods, No-U-Turn Sampler

Related items

Showing items related by title and author.

  • Thumbnail
    Markov Chain Monte Carlo Algorithms for Bayesian Computation, a Survey and Some Generalisation 
    Wu, Changye; Robert, Christian P. (2020) Chapitre d'ouvrage
  • Thumbnail
    Acceleration Strategies of Markov Chain Monte Carlo for Bayesian Computation 
    Wu, Changye (2018-10-04) Thèse
  • Thumbnail
    Discussions on "Riemann manifold Langevin and Hamiltonian Monte Carlo methods" 
    Barthelme, Simon; Beffy, Magali; Chopin, Nicolas; Doucet, Arnaud; Jacob, Pierre E.; Johansen, Adam M.; Marin, Jean-Michel; Robert, Christian P. (2011) Document de travail / Working paper
  • Thumbnail
    Comments on Particle Markov chain Monte Carlo" by C. Andrieu, A. Doucet, and R. Hollenstein" 
    Jacob, Pierre E.; Chopin, Nicolas; Robert, Christian P.; Rue, Havard (2009) Document de travail / Working paper
  • Thumbnail
    On variance stabilisation in population Monte Carlo by double Rao-Blackwellisation 
    Robert, Christian P.; Marin, Jean-Michel; Iacobucci, Alessandra (2010) Article accepté pour publication ou publié
Dauphine PSL Bibliothèque logo
Place du Maréchal de Lattre de Tassigny 75775 Paris Cedex 16
Phone: 01 44 05 40 94
Contact
Dauphine PSL logoEQUIS logoCreative Commons logo