Path-Guiding Algorithms to Improve Efficiency

Modern algorithms for sampling, estimating normalizing constants, and estimating likelihoods often rely on a probability path that connects a complex target distribution to a simple base distribution such as a Gaussian. In this talk, I will discuss the limitations of classical methods, show how using probability paths can improve their efficiency, sometimes dramatically, and present principled strategies for designing the probability path.