Particle Sampling

Particle sampling methods aim to efficiently approximate complex probability distributions, crucial for various applications in machine learning and scientific computing. Recent research focuses on developing novel algorithms, often inspired by physics (e.g., electrostatics, fluid dynamics), that deterministically or stochastically evolve particle systems to converge towards the target distribution, including gradient-based approaches like Stein Variational Gradient Descent (SVGD) and gradient-free alternatives. These advancements improve the accuracy and speed of inference in Bayesian methods, generative modeling, and other areas requiring efficient sampling from high-dimensional spaces, impacting fields like robotics and computer graphics through improved localization and rendering.