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Efficient Bayesian computation for low-photon imaging problems
[article]
2022
arXiv
pre-print
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 attention to situations involving observation noise processes that deviate significantly from Gaussian noise, such as binomial, geometric and low-intensity Poisson noise. These problems are challenging for many reasons. From an inferential viewpoint, low-photon numbers lead to severe identifiability issues, poor stability and
arXiv:2206.05350v1
fatcat:bbcsdj4g5jddfmctpin44dmlk4