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Sampling via Gradient Flows in the Space of Probability Measures
March 12, 2024, 4:45 a.m. | Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M Stuart
cs.LG updates on arXiv.org arxiv.org
Abstract: Sampling a target probability distribution with an unknown normalization constant is a fundamental challenge in computational science and engineering. Recent work shows that algorithms derived by considering gradient flows in the space of probability measures open up new avenues for algorithm development. This paper makes three contributions to this sampling approach by scrutinizing the design components of such gradient flows. Any instantiation of a gradient flow for sampling needs an energy functional and a metric …
abstract algorithm algorithms arxiv challenge computational cs.lg cs.na development distribution engineering gradient math.ds math.na normalization paper probability sampling science shows space stat.ml type via work
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