Mathematics
3 years ago

New non-informative priors revolutionize Bayesian inference for selected parameters!

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arXiv (Cornell University)
Bayesian Selective Inference: Non-informative Priors
Daniel G. Rasines, G. A. Young

Paper Summary

The article discusses how to make accurate predictions when selecting parameters based on data. The researchers analyzed different approaches and suggested using non-informative priors to improve the accuracy of predictions. These priors help create a reliable distribution without prior knowledge and ensure precise predictions for the selected parameter in various scenarios.

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