AI RESEARCH
Forward and inverse problems for measure flows in Bayes Hilbert spaces
arXiv CS.LG
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ArXi:2603.20329v1 Announce Type: cross We study forward and inverse problems for time-dependent probability measures in Bayes--Hilbert spaces. On the forward side, we show that each sufficiently regular Bayes--Hilbert path admits a canonical dynamical realization: a weighted Neumann problem transforms the log-density variation into the unique gradient velocity field of minimum kinetic energy.