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Contrasts and Perspectives

In: Algorithmic Learning in a Random World

Author

Listed:
  • Vladimir Vovk

    (University of London, Royal Holloway)

  • Alexander Gammerman

    (University of London, Royal Holloway)

  • Glenn Shafer

    (Rutgers University)

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.

Suggested Citation

  • Vladimir Vovk & Alexander Gammerman & Glenn Shafer, 2022. "Contrasts and Perspectives," Springer Books, in: Algorithmic Learning in a Random World, edition 2, chapter 0, pages 391-422, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-06649-8_13
    DOI: 10.1007/978-3-031-06649-8_13
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