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Proximal nested sampling was introduced recently to open up Bayesian model selection for high-dimensional problems such as computational imaging.
An Empirical Bayes Approach to Statistics
Robbins, H · 1956
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Nested sampling for general Bayesian computation
Skilling, J · 2006
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A nested sampling algorithm for cosmological model selection
Mukherjee, P.; Parkinson, D.; Liddle, A.R · 2006
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The Bayesian Choice
Robert, C.P · 2007
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Multimodal nested sampling: an efficient and robust alternative to MCMC methods for astronomical data analysis
Feroz, F.; Hobson, M.P · 2008
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MULTINEST: an efficient and robust Bayesian inference tool for cosmology and particle physics
Feroz, F.; Hobson, M.P.; Bridges, M · 2009
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Proximal splitting methods in signal processing
Combettes, P.; Pesquet, J.C · 2011
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Nested sampling with constrained hamiltonian monte carlo
Betancourt, M · 2011
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Tweedie’s formula and selection bias
Efron, B · 2011
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Bayesian computation in big spaces-nested sampling and Galilean Monte Carlo
Skilling, J · 2012
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Proximal algorithms
Parikh, N.; Boyd, S · 2013
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Plug-and-play priors for model based reconstruction
Venkatakrishnan, S.V.; Bouman, C.A.; Wohlberg, B · 2013
Cited alongside, same era.
POLYCHORD: nested sampling for cosmology
Handley, W.J.; Hobson, M.P.; Lasenby, A.N · 2015
Cited alongside, same era.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J.; Weiss, E.; Maheswaranathan, N.; Ganguli, S · 2015
Cited alongside, same era.
Proximal Markov chain Monte Carlo algorithms
Pereyra, M · 2016
Cited alongside, same era.
Efficient Bayesian computation by proximal Markov chain Monte Carlo: when Langevin meets Moreau
Durmus, A.; Moulines, E.; Pereyra, M · 2018
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Song, Y.; Ermon, S · 2019
Cited alongside, same era.
Buchner, J · 2021
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Noise2score: tweedie’s approach to self-supervised image denoising without clean images
Kim, K.; Ye, J.C · 2021
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Nested sampling for physical scientists
Ashton, G.; Bernstein, N.; Buchner, J.; Chen, X.; Csányi, G.; Fowlie, A.; Feroz, F.; Griffiths, M.; Handley, W.; Habeck, M.; et al · 2022
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Machine learning assisted Bayesian model comparison: the learnt harmonic mean estimator
McEwen, J.D.; Wallis, C.G.R.; Price, M.A.; Docherty, M.M · 2022
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Bayesian model comparison for simulation-based inference
Spurio Mancini, A.; Docherty, M.M.; Price, M.A.; McEwen, J.D · 2022
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Plug-and-play methods provably converge with properly trained denoisers
Ryu, E.; Liu, J.; Wang, S.; Chen, X.; Wang, Z.; Yin, W · 2019
Cited alongside, same era.
The IllustrisTNG Simulations: Public Data Release
Nelson, D.; Springel, V.; Pillepich, A.; Rodriguez-Gomez, V.; Torr̃ey, P.; Genel, S.; Vogelsberger, M.; Pakmor, R.; Marinacci, F.; Weinberger, .R.; et al · 2019
Cited alongside, same era.
Improved techniques for training score-based generative models
Song, Y.; Ermon, S · 2020
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Song, Y.; Sohl-Dickstein, J.; Kingma, D.P.; Kumar, A.; Ermon, S.; Poole, B · 2020
Cited alongside, same era.
Proximal nested sampling for high-dimensional Bayesian model selection
Cai, X.; McEwen, J.D.; Pereyra, M · 2022
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Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie
Laumont, R.; Bortoli, V.D.; Almansa, A.; Delon, J.; Durmus, A.; Pereyra, M · 2022
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Improving diffusion models for inverse problems using manifold constraints
Chung, H.; Sim, B.; Ryu, D.; Ye, J.C · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R.; Blattmann, A.; Lorenz, D.; Esser, P.; Ommer, B · 2022
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Learned harmonic mean estimation of the marginal likelihood with normalising flows
Polanska, A.; Price, M.A.; Spurio Mancini, A.; McEwen, J.D · 2023
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