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continuous uniform distribution

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EntityQ274506· pop 45· linked from 468 articles

continuous uniform distribution

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Also 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]}
Pdf
{ 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}}}

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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

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