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- W3160261929 abstract "We discuss Bayesian inference for parameters selected using the data. First, we provide a critical analysis of the existing positions in the literature regarding the correct Bayesian approach under selection. Second, we propose two types of non-informative priors for selection models. These priors may be employed to produce a posterior distribution in the absence of prior information as well as to provide well-calibrated frequentist inference for the selected parameter. We test the proposed priors empirically in several scenarios." @default.
- W3160261929 created "2021-05-24" @default.
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- W3160261929 date "2020-08-11" @default.
- W3160261929 modified "2023-09-27" @default.
- W3160261929 title "Bayesian Selective Inference: Non-informative Priors" @default.
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