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Provably robust score-based diffusion posterior sampling for plug-and-play image reconstruction
In a great number of tasks in science and engineering, the goal is to infer an unknown image
from a small number of noisy measurements collected from a known forward model …
from a small number of noisy measurements collected from a known forward model …
High-dimensional Gaussian sampling: a review and a unifying approach based on a stochastic proximal point algorithm
Efficient sampling from a high-dimensional Gaussian distribution is an old but high-stakes
issue. Vanilla Cholesky samplers imply a computational cost and memory requirements that …
issue. Vanilla Cholesky samplers imply a computational cost and memory requirements that …
Emerging directions in Bayesian computation
Bayesian models are powerful tools for studying complex data, allowing the analyst to
encode rich hierarchical dependencies and leverage prior information. Most importantly …
encode rich hierarchical dependencies and leverage prior information. Most importantly …
A proximal Markov chain Monte Carlo method for Bayesian inference in imaging inverse problems: When Langevin meets Moreau
Modern imaging methods rely strongly on Bayesian inference techniques to solve
challenging imaging problems. Currently, the predominant Bayesian computational …
challenging imaging problems. Currently, the predominant Bayesian computational …
Efficient MCMC sampling with dimension-free convergence rate using ADMM-type splitting
Performing exact Bayesian inference for complex models is computationally intractable.
Markov chain Monte Carlo (MCMC) algorithms can provide reliable approximations of the …
Markov chain Monte Carlo (MCMC) algorithms can provide reliable approximations of the …
Accelerating proximal Markov chain Monte Carlo by using an explicit stabilized method
We present a highly efficient proximal Markov chain Monte Carlo methodology to perform
Bayesian computation in imaging problems. Similarly to previous proximal Monte Carlo …
Bayesian computation in imaging problems. Similarly to previous proximal Monte Carlo …
Regularization by denoising: Bayesian model and Langevin-within-split Gibbs sampling
This paper introduces a Bayesian framework for image inversion by deriving a probabilistic
counterpart to the regularization-by-denoising (RED) paradigm. It additionally implements a …
counterpart to the regularization-by-denoising (RED) paradigm. It additionally implements a …
Global consensus monte carlo
To conduct Bayesian inference with large datasets, it is often convenient or necessary to
distribute the data across multiple machines. We consider a likelihood function expressed as …
distribute the data across multiple machines. We consider a likelihood function expressed as …
Subgradient Langevin Methods for Sampling from Nonsmooth Potentials
This paper is concerned with sampling from probability distributions on admitting a density of
the form, where, with being a linear operator and being nondifferentiable. Two different …
the form, where, with being a linear operator and being nondifferentiable. Two different …
Efficient Bayesian computation for low-photon imaging problems
This paper studies a new and highly efficient Markov chain Monte Carlo (MCMC)
methodology to perform Bayesian inference in low-photon imaging problems, with particular …
methodology to perform Bayesian inference in low-photon imaging problems, with particular …