Feb. 13, 2024, 5:43 a.m. | Julius Vetter Guy Moss Cornelius Schr\"oder Richard Gao Jakob H. Macke

cs.LG updates on arXiv.org arxiv.org

Scientific modeling applications often require estimating a distribution of parameters consistent with a dataset of observations - an inference task also known as source distribution estimation. This problem can be ill-posed, however, since many different source distributions might produce the same distribution of data-consistent simulations. To make a principled choice among many equally valid sources, we propose an approach which targets the maximum entropy distribution, i.e., prioritizes retaining as much uncertainty as possible. Our method is purely sample-based - leveraging …

applications consistent cs.lg data dataset distribution entropy inference modeling parameters sample simulations

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