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The split Gibbs sampler revisited: improvements to its algorithmic structure and augmented target distribution. (arXiv:2206.13894v1 [stat.CO] CROSS LISTED)
June 30, 2022, 1:11 a.m. | Marcelo Pereyra, Luis A. Vargas-Mieles, Konstantinos C. Zygalakis
stat.ML updates on arXiv.org arxiv.org
This paper proposes a new accelerated proximal Markov chain Monte Carlo
(MCMC) methodology to perform Bayesian computation efficiently in imaging
inverse problems. The proposed methodology is derived from the Langevin
diffusion process and stems from tightly integrating two state-of-the-art
proximal Langevin MCMC samplers, SK-ROCK and split Gibbs sampling (SGS), which
employ distinctively different strategies to improve convergence speed. More
precisely, we show how to integrate, at the level of the Langevin diffusion
process, the proximal SK-ROCK sampler which is based …
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