Bayesian Statistics
Seymour Geisser, a proponent of predictive inference, gives predictive applications of Bayesian statistics.
In Bayesian statistics, one can compute (Bayesian) prediction intervals from the posterior probability of the random variable, as a credible interval. In theoretical work, credible intervals are not often calculated for the prediction of future events, but for inference of parameters – i.e., credible intervals of a parameter, not for the outcomes of the variable itself. However, particularly where applications are concerned with possible extreme values of yet to be observed cases, credible intervals for such values can be of practical importance.
Read more about this topic: Prediction Interval
Famous quotes containing the word statistics:
“July 4. Statistics show that we lose more fools on this day than in all the other days of the year put together. This proves, by the number left in stock, that one Fourth of July per year is now inadequate, the country has grown so.”
—Mark Twain [Samuel Langhorne Clemens] (1835–1910)