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08/18/2023
Accelerated Bayesian imaging by relaxed proximal-point Langevin sampling
This paper presents a new accelerated proximal Markov chain Monte Carlo ...
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06/30/2023
Proximal nested sampling with data-driven priors for physical scientists
Proximal nested sampling was introduced recently to open up Bayesian mod...
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06/28/2022
The split Gibbs sampler revisited: improvements to its algorithmic structure and augmented target distribution
This paper proposes a new accelerated proximal Markov chain Monte Carlo ...
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06/11/2022
Learned reconstruction with convergence guarantees
In recent years, deep learning has achieved remarkable empirical success...
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06/10/2022
Efficient Bayesian computation for low-photon imaging problems
This paper studies a new and highly efficient Markov chain Monte Carlo (...
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01/16/2022
On Maximum-a-Posteriori estimation with Plug Play priors and stochastic gradient descent
Bayesian methods to solve imaging inverse problems usually combine an ex...
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06/07/2021
High-dimensional Bayesian model selection by proximal nested sampling
Imaging methods often rely on Bayesian statistical inference strategies ...
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03/18/2021
Bayesian Imaging With Data-Driven Priors Encoded by Neural Networks: Theory, Methods, and Algorithms
This paper proposes a new methodology for performing Bayesian inference ...
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03/08/2021
Bayesian imaging using Plug Play priors: when Langevin meets Tweedie
Since the seminal work of Venkatakrishnan et al. (2013), Plug Play (...
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10/13/2020
Bayesian model selection for unsupervised image deconvolution with structured Gaussian priors
This paper considers the objective comparison of stochastic models to so...
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08/13/2020
Maximum likelihood estimation of regularisation parameters in high-dimensional inverse problems: an empirical Bayesian approach. Part II: Theoretical Analysis
This paper presents a detailed theoretical analysis of the three stochas...
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02/11/2020
Wasserstein Control of Mirror Langevin Monte Carlo
Discretized Langevin diffusions are efficient Monte Carlo methods for sa...
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11/26/2019
Maximum likelihood estimation of regularisation parameters in high-dimensional inverse problems: an empirical Bayesian approach
Many imaging problems require solving an inverse problem that is ill-con...
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08/23/2019
Accelerating proximal Markov chain Monte Carlo by using explicit stabilised methods
We present a highly efficient proximal Markov chain Monte Carlo methodol...
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06/28/2019
Efficient stochastic optimisation by unadjusted Langevin Monte Carlo. Application to maximum marginal likelihood and empirical Bayesian estimation
Stochastic approximation methods play a central role in maximum likeliho...
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03/02/2018
Scalable Bayesian uncertainty quantification in imaging inverse problems via convex optimization
We propose a Bayesian uncertainty quantification method for large-scale ...
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11/13/2017
Uncertainty quantification for radio interferometric imaging: II. MAP estimation
Uncertainty quantification is a critical missing component in radio inte...
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11/13/2017
Uncertainty quantification for radio interferometric imaging: I. proximal MCMC methods
Uncertainty quantification is a critical missing component in radio inte...
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02/05/2015