Monte Carlo guided Diffusion for Bayesian linear inverse problems

1 code implementation • 15 Aug 2023 • Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff, Eric Moulines

Ill-posed linear inverse problems arise frequently in various applications, from computational photography to medical imaging.

Bayesian Inference

State and parameter learning with PaRIS particle Gibbs

no code implementations • 2 Jan 2023 • Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff, Eric Moulines, Jimmy Olsson

The particle-based, rapid incremental smoother PaRIS is a sequential Monte Carlo (SMC) technique allowing for efficient online approximation of expectations of additive functionals under the smoothing distribution in these models.

BR-SNIS: Bias Reduced Self-Normalized Importance Sampling

1 code implementation • 13 Jul 2022 • Gabriel Cardoso, Sergey Samsonov, Achille Thin, Eric Moulines, Jimmy Olsson

This method is a wrapper in the sense that it uses the same proposal samples and importance weights as SNIS, but makes clever use of iterated sampling--importance resampling (ISIR) to form a bias-reduced version of the estimator.