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Discrete uniform distribution

In probability theory and statistics, the discrete uniform distribution is a symmetric probability distribution wherein each of some finite whole number n of outcome values are equally likely to be observed. Thus every one of the n outcome values has equal probability 1/n. Intuitively, a discrete uniform distribution is "a known, finite number of…

Estimation of maximum, Overview & Properties

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CDF
⌊ k ⌋ − a + 1 n {\displaystyle {\frac {\lfloor k\rfloor -a+1}{n}}}
CF
e i a t − e i ( b + 1 ) t n ( 1 − e i t ) {\displaystyle {\frac {e^{iat}-e^{i(b+1)t}}{n(1-e^{it})}}}
Entropy
ln ⁡ ( n ) {\displaystyle \ln(n)}
Excess kurtosis
− 6 ( n 2 + 1 ) 5 ( n 2 − 1 ) {\displaystyle -{\frac {6(n^{2}+1)}{5(n^{2}-1)}}}
Mean
a + b 2 {\displaystyle {\frac {a+b}{2}}}
Median
a + b 2 {\displaystyle {\frac {a+b}{2}}}

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Overview

Estimation of maximum

Random permutation

Properties

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Map overview Semantic statistics

Discrete uniform distribution

Nodes32
Edges31
Triples21
Avg. degree1.94
Density0.0625
Components1

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Discrete uniform distribution

Top relations

related to Estimation of maximum · 5
Discrete uniform distribution → Allied, German, The, UMVU, World War II
CDF · 1
Discrete uniform distribution → ⌊ k ⌋ − a + 1 n {\displaystyle {\frac {\lfloor k\rfloor -a+1}{n}}}
CF · 1
Discrete uniform distribution → e i a t − e i ( b + 1 ) t n ( 1 − e i t ) {\displaystyle {\frac {e^{iat}-e^{i(b+1)t}}{n(1-e^{it})}}}
Entropy · 1
Discrete uniform distribution → ln ⁡ ( n ) {\displaystyle \ln(n)}
Excess kurtosis · 1
Discrete uniform distribution → − 6 ( n 2 + 1 ) 5 ( n 2 − 1 ) {\displaystyle -{\frac {6(n^{2}+1)}{5(n^{2}-1)}}}
Mean · 1
Discrete uniform distribution → a + b 2 {\displaystyle {\frac {a+b}{2}}}
Median · 1
Discrete uniform distribution → a + b 2 {\displaystyle {\frac {a+b}{2}}}
MGF · 1
Discrete uniform distribution → e a t − e ( b + 1 ) t n ( 1 − e t ) {\displaystyle {\frac {e^{at}-e^{(b+1)t}}{n(1-e^{t})}}}
Mode · 1
Discrete uniform distribution → N/A
Notation · 1
Discrete uniform distribution → U { a , b } {\displaystyle {\mathcal {U}}\{a,b\}} or u n i f { a , b } {\displaystyle \mathrm {unif} \{a,b\}}

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Important terminology

uniform distribution discrete probability values die displaystyle maximum integers one finite outcome six-sided possible textstyle parameters frac support sample size

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
Discrete uniform distributionCDF⌊ k ⌋ − a + 1 n {\displaystyle {\frac {\lfloor k\rfloor -a+1}{n}}}1.00infobox
Discrete uniform distributionCFe i a t − e i ( b + 1 ) t n ( 1 − e i t ) {\displaystyle {\frac {e^{iat}-e^{i(b+1)t}}{n(1-e^{it})}}}1.00infobox
Discrete uniform distributionEntropyln ⁡ ( n ) {\displaystyle \ln(n)}1.00infobox
Discrete uniform distributionExcess kurtosis− 6 ( n 2 + 1 ) 5 ( n 2 − 1 ) {\displaystyle -{\frac {6(n^{2}+1)}{5(n^{2}-1)}}}1.00infobox
Discrete uniform distributionMeana + b 2 {\displaystyle {\frac {a+b}{2}}}1.00infobox
Discrete uniform distributionMediana + b 2 {\displaystyle {\frac {a+b}{2}}}1.00infobox
Discrete uniform distributionMGFe a t − e ( b + 1 ) t n ( 1 − e t ) {\displaystyle {\frac {e^{at}-e^{(b+1)t}}{n(1-e^{t})}}}1.00infobox
Discrete uniform distributionModeN/A1.00infobox
Discrete uniform distributionNotationU { a , b } {\displaystyle {\mathcal {U}}\{a,b\}} or u n i f { a , b } {\displaystyle \mathrm {unif} \{a,b\}}1.00infobox
Discrete uniform distributionParametersa , b {\displaystyle a,b} integers with b ≥ a {\displaystyle b\geq a} n = b − a + 1 {\displaystyle n=b-a+1}1.00infobox
Discrete uniform distributionPGFz a − z b + 1 n ( 1 − z ) {\displaystyle {\frac {z^{a}-z^{b+1}}{n(1-z)}}}1.00infobox
Discrete uniform distributionPMF1 n {\displaystyle {\frac {1}{n}}}1.00infobox
Discrete uniform distributionSkewness0 {\displaystyle 0}1.00infobox
Discrete uniform distributionSupportk ∈ { a , a + 1 , … , b − 1 , b } {\displaystyle k\in \{a,a+1,\dots ,b-1,b\}}1.00infobox
Discrete uniform distributionVariance( b − a + 1 ) 2 − 1 12 {\displaystyle {\frac {(b-a+1)^{2}-1}{12}}}1.00infobox
Discrete uniform distributionis asymmetric probability distribution wherein each of some finite whole number n of outcome values are equally likely to be observed0.90text

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