Speaker
Florent Leclercq
(Institut d'Astrophysique de Paris)
Description
-
Introduction to field-level inference
-
Part 1: Bayesian signal processing
- Gaussian random fields
- Bayesian signal processing and Wiener filtering (denoising)
- Wiener filtering for linear field-level inference
- Practical work: Bayesian denoising via Wiener filtering
-
Part 2: Markov Chain Monte Carlo techniques
- The Metropolis-Hastings algorithm
- Markov Chain Monte Carlo Beyond Metropolis-Hastings
- Field-level inference with non-linear models
- Practical work: Field-level inference with a non-linear model