Contrasts and Perspectives

Vladimir Vovk, Alexander Gammerman and Glenn Shafer
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Vladimir Vovk: University of London, Royal Holloway
Alexander Gammerman: University of London, Royal Holloway
Glenn Shafer: Rutgers University

Chapter Chapter 13 in Algorithmic Learning in a Random World, 2022, pp 391-422 from Springer

Abstract: Abstract This book has emphasized conformal prediction under statistical randomness, the standard assumption in machine learning, and only in this part (starting from Chap. 11 ) have we extended it to substantially different statistical models. Interestingly, conformal prediction in this wider sense is much more familiar to statisticians. In this concluding chapter, we step back to survey the historical context of conformal prediction, contrasting it with classical inductive, transductive, and Bayesian methods. In the previous two chapters we generalized conformal and Venn prediction to online compression models. In this chapter we will complement this by discussing conformal predictive distributions in the Gaussian model, which were introduced by Fisher under the name of fiducial predictive distributions. In conclusion, we review some of the new work on conformal prediction. Relaxing the assumption of randomness and replacing it by other assumptions have been recurring themes in this work.

Keywords: Inductive learning; Transductive learning; Fiducial prediction; Bayesian learning; Conformal prediction (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-06649-8_13

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DOI: 10.1007/978-3-031-06649-8_13

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