In Bayesian statistics a prior distribution formalizes information or knowledge (often subjective), available before a sample is seen, in the form of a probability distribution. A distribution with large spread is used when little is known about the parameter(s), while a more narrow prior distribution represents a greater degree of information.
In Bayesian statistics a prior distribution formalizes information or knowledge (often subjective) about uncertain quantities which is available before a sample is seen, in the form of a probability distribution.
Prior distributions play an important role in Bayesian analysis; prior knowledge is updated by data via the likelihood for Bayesian inference.
Priors may be informative (especially valuable when information from previous
data is available, or other important outside knowledge should be incorporated),
or vague/diffuse. A distribution with large spread may be used when little is known about the parameter(s), while a more narrow prior distribution represents a greater degree of information.
