Target Density

Target density estimation focuses on efficiently sampling from complex, often high-dimensional probability distributions, a crucial task in various scientific fields. Current research emphasizes developing advanced sampling algorithms, such as normalizing flows and score-based methods, often incorporating techniques like tempered distributions and annealed importance sampling to overcome challenges posed by multimodality and high dimensionality. These improvements enable more accurate estimations of target densities, with applications ranging from molecular simulations and image generation to Bayesian inference and tracking problems. The resulting advancements significantly impact fields relying on accurate probability distribution characterization.