Sequential Monte Carlo

Sequential Monte Carlo (SMC) methods are powerful tools for Bayesian inference, aiming to approximate complex probability distributions by iteratively sampling and weighting particles. Current research focuses on improving SMC efficiency and accuracy, particularly for high-dimensional problems and multimodal distributions, through techniques like persistent sampling, twisted objectives, and the integration of differentiable particle filters with neural networks. These advancements are impacting diverse fields, enabling more robust Bayesian inference in applications ranging from parameter estimation in nonlinear systems and language model control to online classification and experimental design.