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© The Author(s) 2020.Traditionally, statistical processes being derived via analytic calculations whose validity frequently utilizes test dimensions developing to infinity. We utilize tools from deep learning to develop a new approach, adversarial Monte Carlo meta-learning, for making optimal analytical treatments. Statistical problems tend to be framed as two-player games for which Nature adversarially chooses a distribution which makes it hard for a statistician to answer the scientif
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