Particle Based Algorithm

Particle-based algorithms are computational methods that represent probability distributions using a set of particles, enabling efficient estimation and optimization in complex scenarios. Current research focuses on improving the efficiency and robustness of these algorithms, particularly through advancements in particle filtering techniques (e.g., Stein variational gradient descent, multiple update methods), and their application to diverse problems like maximum likelihood estimation in latent variable models and anomaly detection. These methods offer significant advantages in high-dimensional spaces and constrained domains, impacting fields ranging from robotics (localization) to machine learning (model training) and physics (anomaly detection).