Non-Equilibrium Sampling

vendredi 21 janvier 2022, 9h30 - 10h30

Sampling from a complex distributionπand approximating its intractable normalizing constant $$Z$$ are challenging problems. In this talk, a novel family of importance samplers (IS) and Markov chain Monte Carlo (MCMC) samplers is derived. Given an invertible map $$T$$, these schemes combine (with weights) elements from the forward and backward orbits through points sampled from a proposal distribution $$\rho$$. The map $$T$$ does not leave the target $$\pi$$ invariant, hence the name NEO, standing for Non-Equilibrium Orbits. NEO-IS provides unbiased estimators of the normalizing constant and self-normalized IS estimators of expectations under $$\pi$$ while NEO-MCMC combines multiple NEO-IS estimates of the normalizing constant and an iterated sampling-importance resampling mechanism to sample from $$\pi$$.

joint work with Achille Thin, Yazid Janati, Sylvain Le Corff, Charles Ollion, Arnaud Doucet, Eric Moulines, Christian Robert

https://u-paris.zoom.us/j/82245688369?pwd=WjZFRWZxL2QwYWoyMGVLZEp2dzAvQT09