Estimating the probability of low-likelihood, high-impact events in science, engineering, and finance is challenging because brute-force Monte Carlo sampling requires an excessive volume of model iterations.
The article introduces a method using guided generative models to address the inefficiency of running a model repeatedly with randomly drawn inputs to estimate rare outcomes.