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continuous uniform distribution
Sign in to saveAlso known as rectangular distribution, uniform distribution
uniform distribution on an interval
Key facts
- Notation
- U [ a , b ] {\displaystyle {\mathcal {U}}_{[a,b]}}
- Parameters
- − ∞ < a < b < ∞ {\displaystyle -\infty <a<b<\infty }
- Support
- [ a , b ] {\displaystyle [a,b]}
- { 1 b − a for x ∈ [ a , b ] 0 otherwise {\displaystyle {\begin{cases}{\frac {1}{b-a}}&{\text{for }}x\in [a,b]\\0&{\text{otherwise}}\end{cases}}}
- Cdf
- b\n \\end{cases}"}}'> { 0 for x < a x − a b − a for x ∈ [ a , b ] 1 for x > b {\displaystyle {\begin{cases}0&{\text{for }}x<a\\{\frac {x-a}{b-a}}&{\text{for }}x\in [a,b]\\1&{\text{for }}x>b\end{cases}}}
- Mean
- 1 2 ( a + b ) {\displaystyle {\tfrac {1}{2}}(a+b)}
- Median
- 1 2 ( a + b ) {\displaystyle {\tfrac {1}{2}}(a+b)}
- Mode
- any value in ( a , b ) {\displaystyle {\text{any value in }}(a,b)}
- Variance
- 1 12 ( b − a ) 2 {\displaystyle {\tfrac {1}{12}}(b-a)^{2}}
- Mad
- 1 4 ( b − a ) {\displaystyle {\tfrac {1}{4}}(b-a)}
- Skewness
- 0 {\displaystyle 0}
- Excess kurtosis
- − 6 5 {\displaystyle -{\tfrac {6}{5}}}
- Entropy
- log ( b − a ) {\displaystyle \log(b-a)}
- Mgf
- { e t b − e t a t ( b − a ) for t ≠ 0 1 for t = 0 {\displaystyle {\begin{cases}{\frac {\mathrm {e} ^{tb}-\mathrm {e} ^{ta}}{t(b-a)}}&{\text{for }}t\neq 0\\1&{\text{for }}t=0\end{cases}}}
- Cf
- { e i t b − e i t a i t ( b − a ) for t ≠ 0 1 for t = 0 {\displaystyle {\begin{cases}{\frac {\mathrm {e} ^{\mathrm {i} tb}-\mathrm {e} ^{\mathrm {i} ta}}{\mathrm {i} t(b-a)}}&{\text{for }}t\neq 0\\1&{\text{for }}t=0\end{cases}}}
via Wikipedia infobox
Wikidata facts
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- Commons category
- Uniform distribution
Sources (3)
via Wikidata · CC0
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Article
In probability theory and statistics, the continuous uniform distributions or rectangular distributions are a family of symmetric probability distributions. Such a distribution describes an experiment where there is an arbitrary outcome that lies between certain bounds. The bounds are defined by the parameters,
a
Gallery (2)
Connections
interval
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probability distribution
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maximum likelihood estimation
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moment
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discrete uniform distribution
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rectangular function
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Box–Muller transform
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inverse transform sampling
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World War II
Concept
statistics
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International Standard Book Number
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programming language
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digital object identifier
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probability theory
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Gerolamo Cardano
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normal distribution
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Library of Congress Control Number
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median
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random variable
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variance
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