Christian P. Robert

Faster Hamiltonian Monte Carlo by learning leapfrog scale: an offline randomized solution

Abstract We introduce a Hamiltonian Monte Carlo (HMC) methodology based on an offline empirical calibration of randomized leapfrog parameters. The approach, referred to as eHMC, where e stands for empirical, leverages importance sampling to construct an empirical distribution on...

Prequential posteriors

Abstract Data assimilation is a fundamental task in updating forecasting models upon observing new data, with applications ranging from weather prediction to online reinforcement learning. Deep generative forecasting models (DGFMs) have shown excellent performance in these areas,...