Denoising the Future: Top-p Distributions for Moving Through Time

ArXi:2506.07578v4 Announce Type: replace-cross Inference in dynamic probabilistic models is a complex task involving expensive operations. In particular, for Hidden Marko Models, the whole state space has to be enumerated for advancing in time. Even states with negligible probabilities are considered, resulting in computational inefficiency and possibly increased noise due to the propagation of unlikely probability mass. We propose to denoise the future and speed up inference by using only the top-p transitions, i.e., the most probable transitions with accumulated probability p.