• 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

Component-wise approximate Bayesian computation via Gibbs-like steps

Clarté, Grégoire; Robert, Christian P.; Ryder, Robin; Stoehr, Julien (2021), Component-wise approximate Bayesian computation via Gibbs-like steps, Biometrika, 108, 3, p. 591–607. 10.1093/biomet/asaa090

View/Open
1905.13599.pdf (329.8Kb)
Type
Article accepté pour publication ou publié
Date
2021
Journal name
Biometrika
Volume
108
Number
3
Publisher
Oxford University Press
Pages
591–607
Publication identifier
10.1093/biomet/asaa090
Metadata
Show full item record
Author(s)
Clarté, Grégoire
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Robert, Christian P. cc
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Ryder, Robin
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Stoehr, Julien cc
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Abstract (EN)
Approximate Bayesian computation methods are useful for generative models with intractable likelihoods. These methods are however sensitive to the dimension of the parameter space, requiring exponentially increasing resources as this dimension grows. To tackle this difficulty, we explore a Gibbs version of the Approximate Bayesian computation approach that runs component-wise approximate Bayesian computation steps aimed at the corresponding conditional posterior distributions, and based on summary statistics of reduced dimensions. While lacking the standard justifications for the Gibbs sampler, the resulting Markov chain is shown to converge in distribution under some partial independence conditions. The associated stationary distribution can further be shown to be close to the true posterior distribution and some hierarchical versions of the proposed mechanism enjoy a closed form limiting distribution. Experiments also demonstrate the gain in efficiency brought by the Gibbs version over the standard solution.
Subjects / Keywords
simulation; curse of dimensionality; conditional distributions; convergence of Markov chains; generative model; Gibbs sampler; hierarchical Bayes model; incompatible conditionals; likelihood-free inference

Related items

Showing items related by title and author.

  • Thumbnail
    Component-wise approximate Bayesian computation via Gibbs-like steps 
    Clarté, Grégoire; Ryder, Robin J.; Robert, Christian P.; Stoehr, Julien (2019) Document de travail / Working paper
  • Thumbnail
    Some discussions of D. Fearnhead and D. Prangle's Read Paper "Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation" 
    Singh, Sumeetpal S.; Sedki, Mohammed; Jasra, Ajay; Pudlo, Pierre; Robert, Christian P.; Lee, Anthony; Marin, Jean-Michel; Kosmidis, Ioannis; Girolami, Mark; Andrieu, Christophe; Cornebise, Julien; Doucet, Arnaud; Barthelme, Simon; Chopin, Nicolas (2012) Article accepté pour publication ou publié
  • Thumbnail
    Approximate Bayesian Computational methods 
    Marin, Jean-Michel; Pudlo, Pierre; Robert, Christian P.; Ryder, Robin J. (2012) Article accepté pour publication ou publié
  • Thumbnail
    Comment: Approximate Bayesian Computation - Sequential Quasi Monte Carlo 
    Ryder, Robin J. (2015) Article accepté pour publication ou publié
  • Thumbnail
    A Phylogenetic Model of the Evolution of Discrete Matrices for the Joint Inference of Lexical and Phonological Language Histories 
    Clarté, Grégoire; Ryder, Robin J. (2022) Document de travail / Working paper
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