Asymptotically Exact Data Augmentation: Models, Properties, and Algorithms

DOI10.1080/10618600.2020.1826954OpenAlexW3099094880MaRDI QIDQ5066390

Nicolas Dobigeon, Pierre Chainais, Maxime Vono

Publication date: 29 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1902.05754

zbMATH Keywords

robustnessapproximationBayesian inferenceauxiliary variablesdivide-and-conquer

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (2)

The divide-and-conquer sequential Monte Carlo algorithm: theoretical properties and limit theorems ⋮ High-Dimensional Gaussian Sampling: A Review and a Unifying Approach Based on a Stochastic Proximal Point Algorithm

Uses Software

• KernSmooth
• UNLocBoX
• RecPF
• dobson
• VIREL

Cites Work

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• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
• The Pólya-gamma Gibbs sampler for Bayesian logistic regression is uniformly ergodic
• Marginal maximum a posteriori estimation using Markov chain Monte Carlo
• Maximum likelihood estimation of a log-concave density and its distribution function: basic properties and uniform consistency
• Fixed-point algorithms for inverse problems in science and engineering. Based on the presentations at the interdisciplinary workshop, BIRS, Banff, Canada, November 1--6, 2009.
• An adaptive sequential Monte Carlo method for approximate Bayesian computation
• On Dirac delta sequences and their generating functions
• Likelihood-free parallel tempering
• From EM to data augmentation: the emergence of MCMC Bayesian computation in the 1980s
• An algorithm for total variation minimization and applications
• A general method for robust Bayesian modeling
• Random gradient-free minimization of convex functions
• Nonasymptotic convergence analysis for the unadjusted Langevin algorithm
• Proximal Markov chain Monte Carlo algorithms
• Penalized regression, standard errors, and Bayesian Lassos
• The Ordered Subsets Mirror Descent Optimization Method with Applications to Tomography
• Outlier Detection Using Nonconvex Penalized Regression
• A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
• Maximum Likelihood Estimation from Incomplete Data
• The Bayesian Lasso
• The Calculation of Posterior Distributions by Data Augmentation
• Auxiliary Variable Methods for Markov Chain Monte Carlo with Applications
• Gibbs Sampling for Bayesian Non-Conjugate and Hierarchical Models by Using Auxiliary Variables
• Generalized Nonlinear Modeling With Multivariate Free-Knot Regression Splines
• Split-and-Augmented Gibbs Sampler—Application to Large-Scale Inference Problems
• Constructing Summary Statistics for Approximate Bayesian Computation: Semi-Automatic Approximate Bayesian Computation
• Efficient Bayesian Computation by Proximal Markov Chain Monte Carlo: When Langevin Meets Moreau
• Bayesian Analysis of Binary and Polychotomous Response Data
• Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables
• Total Generalized Variation
• A Useful Convergence Theorem for Probability Distributions

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