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In Bayesian statistics, a credible interval is an interval in the domain of a posterior probability distribution or predictive distribution used for interval estimation.^{[1]} The generalisation to multivariate problems is the credible region. Credible intervals are analogous to confidence intervals in frequentist statistics,^{[2]} although they differ on a philosophical basis;^{[3]} Bayesian intervals treat their bounds as fixed and the estimated parameter as a random variable, whereas frequentist confidence intervals treat their bounds as random variables and the parameter as a fixed value.
For example, in an experiment that determines the uncertainty distribution of parameter t, if the probability that t lies between 35 and 45 is 0.95, then 35 \le t \le 45 is a 95% credible interval.
Credible intervals are not unique on a posterior distribution. Methods for defining a suitable credible interval include:
It is possible to frame the choice of a credible interval within decision theory and, in that context, an optimal interval will always be a highest probability density set.^{[4]}
A frequentist 95% confidence interval means that with a large number of repeated samples, 95% of such calculated confidence intervals would include the true value of the parameter. The probability that the parameter is inside the given interval (say, 35–45) is either 0 or 1 (the non-random unknown parameter is either there or not). In frequentist terms, the parameter is fixed (cannot be considered to have a distribution of possible values) and the confidence interval is random (as it depends on the random sample). Antelman (1997, p. 375) summarizes a [95%] confidence interval as "... one interval generated by a procedure that will give correct intervals 95% of the time".^{[5]}
In general, Bayesian credible intervals do not coincide with frequentist confidence intervals for two reasons:
For the case of a single parameter and data that can be summarised in a single sufficient statistic, it can be shown that the credible interval and the confidence interval will coincide if the unknown parameter is a location parameter (i.e. the forward probability function has the form \mathrm{Pr}(x|\mu) = f(x - \mu) ), with a prior that is a uniform flat distribution;^{[6]} and also if the unknown parameter is a scale parameter (i.e. the forward probability function has the form \mathrm{Pr}(x|s) = f(x/s) ), with a Jeffreys' prior \mathrm{Pr}(s|I) \;\propto\; 1/s ^{[6]} — the latter following because taking the logarithm of such a scale parameter turns it into a location parameter with a uniform distribution. But these are distinctly special (albeit important) cases; in general no such equivalence can be made.
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