Uniform Probability Distribution: Difference between revisions
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* (Wikipedia, 2009) ⇒ http://en.wikipedia.org/wiki/uniform_distribution Retrieved:2009-6-21. | * (Wikipedia, 2009) ⇒ http://en.wikipedia.org/wiki/uniform_distribution Retrieved:2009-6-21. | ||
** Uniform distribution can refer to: | ** Uniform distribution can refer to: | ||
*** [[discrete uniform distribution]] | *** [[discrete uniform distribution]]. | ||
*** [[continuous uniform distribution]]. | *** [[continuous uniform distribution]]. | ||
** They share the property that they have a bounded range, and are weakly unimodal where any members of their support can be taken to be the mode. <P> In Bayesian statistics, some users assume [[unbounded uniform prior distribution]]s (as a translation invariant Jeffreys prior), which are improper priors. For instance, [[maximum likelihood estimation]] can be interpreted as maximum a posteriori estimation with a uniform prior, even if the resulting distribution is improper. | ** They share the property that they have a bounded range, and are weakly unimodal where any members of their support can be taken to be the mode. <P> In Bayesian statistics, some users assume [[unbounded uniform prior distribution]]s (as a translation invariant Jeffreys prior), which are improper priors. For instance, [[maximum likelihood estimation]] can be interpreted as maximum a posteriori estimation with a uniform prior, even if the resulting distribution is improper. | ||
Latest revision as of 00:45, 24 July 2023
See: Discrete Uniform Distribution, Continuous Uniform Distribution, Uniform Probability Function, Symmetric Probability Distribution, Skewed Probability Distribution, Unimodal Distribution, Multimodal Distribution.
References
2009
- (Wikipedia, 2009) ⇒ http://en.wikipedia.org/wiki/uniform_distribution Retrieved:2009-6-21.
- Uniform distribution can refer to:
- They share the property that they have a bounded range, and are weakly unimodal where any members of their support can be taken to be the mode.
In Bayesian statistics, some users assume unbounded uniform prior distributions (as a translation invariant Jeffreys prior), which are improper priors. For instance, maximum likelihood estimation can be interpreted as maximum a posteriori estimation with a uniform prior, even if the resulting distribution is improper.