Difference between revisions of "Predictive Inference"

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A [[Predictive Inference]] is a [[statistical inference]] that involves [[prediction]] of [[future observation]]s based on past [[observation]]s.
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A [[Predictive Inference]] is a [[data-driven inference]] that involves [[prediction]] of [[future observation]]s based on past [[observation]]s.
 
* <B>Example(s):</B>
 
* <B>Example(s):</B>
 
** [[Predictive Model]],
 
** [[Predictive Model]],
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** [[Regularization Task]];
 
** [[Regularization Task]];
 
** [[Learning Task]].
 
** [[Learning Task]].
<B> See:</B> [[Signal Processing Task]], [[Data Processing Task]], .
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<B> See:</B> [[Statistical Inference]], [[Signal Processing Task]], [[Data Processing Task]], .
 
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Latest revision as of 08:11, 14 June 2019

A Predictive Inference is a data-driven inference that involves prediction of future observations based on past observations.

See: Statistical Inference, Signal Processing Task, Data Processing Task, .



References

2017

  • (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Predictive_inference Retrieved:2017-9-9.
    • 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]