Anas Cherradi, Yazid Janati, Alain Durmus, Sylvain Le Corff, Yohan Petetin
Importance sampling is a Monte Carlo method which designs estimators of expectations under a target distribution using weighted samples from a proposal distribution. When the target distribution is complex, such as multimodal distributions in highdimensional spaces, the efficiency of importance sampling critically depends on the choice of the proposal distribution. In this paper, we propose a novel adaptive scheme for the construction of efficient proposal distributions. Our algorithm promotes efficient exploration of the target distribution by combining global sampling mechanisms with a delayed weighting procedure. The proposed weighting mechanism plays a key role by enabling rapid resampling in regions where the proposal distribution is poorly adapted to the target. Our sampling algorithm is shown to be geometrically convergent under mild assumptions and is illustrated through various numerical experiments.
Quantitative mode stability for the wave equation on the Kerr-Newman spacetime
Risk-Aware Objective-Based Forecasting in Inertia Management
Chainalysis: Geography of Cryptocurrency 2023
Periodicity in Cryptocurrency Volatility and Liquidity
Impact of Geometric Uncertainty on the Computation of Abdominal Aortic Aneurysm Wall Strain
Simulation-based Bayesian inference with ameliorative learned summary statistics -- Part I