# Predictive inference

**Predictive inference** is an approach to statistical inference that emphasizes the prediction of future observations based on past observations.

Initially, predictive inference was based on *observable* parameters and it was the main purpose of studying probability, but it fell out of favor in the 20th century due to a new parametric approach pioneered by Bruno de Finetti. The approach modeled phenomena as a physical system observed with error (e.g., celestial mechanics). De Finetti's idea of exchangeability—that future observations should behave like past observations—came to the attention of the English-speaking world with the 1974 translation from French of his 1937 paper,[1] and has since been propounded by such statisticians as Seymour Geisser.[2]

## References

- De Finetti, Bruno (1937). "La Prévision: ses lois logiques, ses sources subjectives".
*Annales de l'Institut Henri Poincaré*.**7**(1): 1–68. ISSN 0365-320X. Translated in "Foresight: Its Logical Laws, Its Subjective Sources".*Breakthroughs in Statistics*. Springer Series in Statistics. 1992. pp. 134–174. doi:10.1007/978-1-4612-0919-5_10. - Geisser, Seymour (1993)
*Predictive Inference: An Introduction*, CRC Press. ISBN 0-412-03471-9