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,[citation needed] 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]

## See also

* Prediction interval
* Predictive analytics
* Predictive modelling

## References

1. ↑ 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.
2. ↑ Geisser, Seymour (1993) Predictive Inference: An Introduction, CRC Press. ISBN 0-412-03471-9

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