Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting

ICML 2020 Niccolò DalmassoRafael IzbickiAnn B. Lee

Parameter estimation, statistical tests and confidence sets are the cornerstones of classical statistics that allow scientists to make inferences about the underlying process that generated the observed data. A key question is whether one can still construct hypothesis tests and confidence sets with proper coverage and high power in a so-called likelihood-free inference (LFI) setting; that is, a setting where the likelihood is not explicitly known but one can forward-simulate observable data according to a stochastic model... (read more)

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