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Continuous uniform distribution

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 {\displaystyle a} and b , {\displaystyle…

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CDF
{ 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}}}
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)}}&{\…
Entropy
log ⁡ ( b − a ) {\displaystyle \log(b-a)}
Excess kurtosis
− 6 5 {\displaystyle -{\tfrac {6}{5}}}
MAD
1 4 ( b − a ) {\displaystyle {\tfrac {1}{4}}(b-a)}
Mean
1 2 ( a + b ) {\displaystyle {\tfrac {1}{2}}(a+b)}

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Related distributions

Occurrence and applications

Random variate generation

History

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Map overview Semantic statistics

Continuous uniform distribution

Nodes85
Edges84
Triples33
Avg. degree1.98
Density0.023529
Components1

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Continuous uniform distribution

Top relations

related to Probability density function · 5
Continuous uniform distribution → Also, Fourier, In, Sometimes, The
related to Standard uniform distribution · 5
Continuous uniform distribution → If, In, One, The, This
related to Cumulative distribution function · 2
Continuous uniform distribution → Its, The
related to Relationship to other functions · 2
Continuous uniform distribution → As, Heaviside
CDF · 1
Continuous uniform distribution → { 0 for x a x − a b − a for x ∈ [ a , b ] 1 for x b {\displaystyle {\begin{cases}0&{\text{for }}xa\\{\frac {x-a}{b-a}}&{\text{for }}x\in [a,b]\\1&{\text{for }}xb\end{cases}}}
CF · 1
Continuous uniform distribution → { 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)}}&{\…
Entropy · 1
Continuous uniform distribution → log ⁡ ( b − a ) {\displaystyle \log(b-a)}
Excess kurtosis · 1
Continuous uniform distribution → − 6 5 {\displaystyle -{\tfrac {6}{5}}}
MAD · 1
Continuous uniform distribution → 1 4 ( b − a ) {\displaystyle {\tfrac {1}{4}}(b-a)}
Mean · 1
Continuous uniform distribution → 1 2 ( a + b ) {\displaystyle {\tfrac {1}{2}}(a+b)}

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Important terminology

distribution displaystyle uniform function continuous frac probability b-a random maximum tfrac interval support mean density variable cases text standard distributions

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
Continuous uniform distributionCDF{ 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}}}1.00infobox
Continuous uniform distributionCF{ 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)}}&{\…1.00infobox
Continuous uniform distributionEntropylog ⁡ ( b − a ) {\displaystyle \log(b-a)}1.00infobox
Continuous uniform distributionExcess kurtosis− 6 5 {\displaystyle -{\tfrac {6}{5}}}1.00infobox
Continuous uniform distributionMAD1 4 ( b − a ) {\displaystyle {\tfrac {1}{4}}(b-a)}1.00infobox
Continuous uniform distributionMean1 2 ( a + b ) {\displaystyle {\tfrac {1}{2}}(a+b)}1.00infobox
Continuous uniform distributionMedian1 2 ( a + b ) {\displaystyle {\tfrac {1}{2}}(a+b)}1.00infobox
Continuous uniform distributionMGF{ 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…1.00infobox
Continuous uniform distributionModeany value in ( a , b ) {\displaystyle {\text{any value in }}(a,b)}1.00infobox
Continuous uniform distributionNotationU [ a , b ] {\displaystyle {\mathcal {U}}_{[a,b]}}1.00infobox
Continuous uniform distributionParameters− ∞ < a < b < ∞ {\displaystyle -\infty <a<b<\infty }1.00infobox
Continuous uniform distributionPDF{ 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}}}1.00infobox
Continuous uniform distributionSkewness0 {\displaystyle 0}1.00infobox
Continuous uniform distributionSupport[ a , b ] {\displaystyle [a,b]}1.00infobox
Continuous uniform distributionVariance1 12 ( b − a ) 2 {\displaystyle {\tfrac {1}{12}}(b-a)^{2}}1.00infobox

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