Bayesian Selective Inference: Non-informative Priors

Daniel G. Rasines; G. Alastair Young

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arXiv · 2008.04584

Bayesian Selective Inference: Non-informative Priors

Daniel G. Rasines·G. Alastair Young

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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.

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Daniel G. Rasines, G. Alastair Young. 2021-05-11. Bayesian Selective Inference: Non-informative Priors. https://arxiv.org/abs/2008.04584

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