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Nonparametric likelihood-free inference with Jensen-Shannon divergence for simulator-based models with categorical output. (arXiv:2205.10890v2 [stat.ME] UPDATED)
May 27, 2022, 1:11 a.m. | Jukka Corander, Ulpu Remes, Ida Holopainen, Timo Koski
cs.LG updates on arXiv.org arxiv.org
Likelihood-free inference for simulator-based statistical models has recently
attracted a surge of interest, both in the machine learning and statistics
communities. The primary focus of these research fields has been to approximate
the posterior distribution of model parameters, either by various types of
Monte Carlo sampling algorithms or deep neural network -based surrogate models.
Frequentist inference for simulator-based models has been given much less
attention to date, despite that it would be particularly amenable to
applications with big data where …
More from arxiv.org / cs.LG updates on arXiv.org
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