June 11, 2024, 4:44 a.m. | David Berghaus, Kostadin Cvejoski, Patrick Seifner, Cesar Ojeda, Ramses J. Sanchez

stat.ML updates on arXiv.org arxiv.org

arXiv:2406.06419v1 Announce Type: cross
Abstract: Markov jump processes are continuous-time stochastic processes which describe dynamical systems evolving in discrete state spaces. These processes find wide application in the natural sciences and machine learning, but their inference is known to be far from trivial. In this work we introduce a methodology for zero-shot inference of Markov jump processes (MJPs), on bounded state spaces, from noisy and sparse observations, which consists of two components. First, a broad probability distribution over families of …

abstract application arxiv continuous cs.lg foundation inference machine machine learning markov methodology natural processes spaces state stat.ml stochastic systems type work zero-shot

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