General-purpose post-sampling reweighting method for multimodal target measures
Abstract
When sampling multi-modal probability distributions, correctly estimating the relative probability of each mode, even when the modes have been discovered and locally sampled, remains challenging. We test a simple reweighting scheme designed for this situation, which consists in minimizing (in terms of weights) the Kullback-Leibler divergence of a weighted (regularized) empirical distribution of the samples with respect to the target measure.
Fields of Study & Subjects
Markov Chains and Monte Carlo Methods
Gaussian Processes and Bayesian Inference
Bayesian Methods and Mixture Models
Publication details
- Journal / source
- arXiv (Cornell University)
- Publication date
- 12 February 2026
- Language
- EN
- Document type
- Preprint