Editing Prior probability (section)

== Informative priors ==
An ''informative prior'' expresses specific, definite information about a variable.
An example is a prior distribution for the temperature at noon tomorrow.
A reasonable approach is to make the prior a [[normal distribution]] with [[expected value]] equal to today's noontime temperature, with [[variance]] equal to the day-to-day variance of atmospheric temperature,
or a distribution of the temperature for that day of the year.

This example has a property in common with many priors, namely, that the posterior from one problem (today's temperature) becomes the prior for another problem (tomorrow's temperature); pre-existing evidence which has already been taken into account is part of the prior and, as more evidence accumulates, the posterior is determined largely by the evidence rather than any original assumption, provided that the original assumption admitted the possibility of what the evidence is suggesting. The terms "prior" and "posterior" are generally relative to a specific datum or observation.

===Strong prior===
A '''strong prior''' is a preceding assumption, theory, concept or idea upon which, after taking account of new information, a current assumption, theory, concept or idea is founded.{{Citation needed|reason=I've seen usages where a strong prior meant "not a weak prior" or was a synonym for a "highly  informative prior." A quick web search only found this page and copycats with this usage.|date=January 2024}} A strong prior is a type of informative prior in which the information contained in the prior distribution dominates the information contained in the data being analyzed. The [[Bayesian analysis]] combines the information contained in the prior with that extracted from the data to produce the [[posterior distribution]] which, in the case of a "strong prior", would be little changed from the prior distribution.