Tractable Initial Distribution

Tractable initial distributions are crucial for efficiently sampling from complex, high-dimensional probability distributions, a common challenge in machine learning and statistical inference. Current research focuses on developing novel model architectures, such as probabilistic circuits and squared neural families, that offer closed-form solutions or efficient approximations, and on improving sampling algorithms like annealed importance sampling (AIS) through techniques like constant rate discretization and score-based diffusion. These advancements enable more accurate and computationally feasible inference in various applications, including density estimation, conditional density estimation, and marginal likelihood estimation. The resulting improvements in tractability and efficiency are significant for advancing probabilistic modeling across diverse scientific fields.