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<!--- EDITORS! Please see [[Wikipedia:WikiProject Probability#Standards]] for a discussion of standards used for probability distribution articles such as this one --->
{{Probability distribution|
  name      =discrete uniform|
  type      =mass|
  pdf_image  =[[Image:Uniform discrete pmf svg.svg|325px|Discrete uniform probability mass function for ''n'' = 5]]<br /><small>''n'' = 5 where ''n'' = ''b''&nbsp;&minus;&nbsp;''a''&nbsp;+&nbsp;1</small>|
  cdf_image  =[[Image:Dis Uniform distribution CDF.svg|325px|Discrete uniform cumulative distribution function for ''n'' = 5]]<br /><small></small>|
  parameters =<math>a \in (\dots,-2,-1,0,1,2,\dots)\,</math><br /><math>b \in (\dots,-2,-1,0,1,2,\dots), b \ge a</math><br /><math>n=b-a+1\,</math>|
  support    =<math>k \in \{a,a+1,\dots,b-1,b\}\,</math>|
  pdf        =<math>\frac{1}{n}</math>|
  cdf        =<math> \frac{\lfloor k \rfloor -a+1}{n} </math>|
  mean      =<math>\frac{a+b}{2}\,</math>|
  median    =<math>\frac{a+b}{2}\,</math>|
  mode      =N/A|
  variance  =<math>\frac{n^2-1}{12}</math>|
  skewness  =<math>0\,</math>|
  kurtosis  =<math>-\frac{6(n^2+1)}{5(n^2-1)}\,</math>|
  entropy    =<math>\ln(n)\,</math>|
  mgf        =<math>\frac{e^{at}-e^{(b+1)t}}{n(1-e^t)}\,</math>|
  char      =<math>\frac{e^{iat}-e^{i(b+1)t}}{n(1-e^{it})}</math>|
}}
 
In [[probability theory]] and [[statistics]], the '''discrete uniform distribution''' is a [[Symmetric distribution|symmetric]] [[discrete probability distribution|probability distribution]] whereby a finite number of values are equally likely to be observed; every one of ''n'' values has equal probability ''1/n''. Another way of saying "discrete uniform distribution" would be "a known, finite number of outcomes equally likely to happen".
 
A simple example of the discrete uniform distribution is throwing a fair {{dice}}. The possible values are 1, 2, 3, 4, 5, 6, and each time the {{dice}} is thrown the probability of a given score is 1/6. If two dice are thrown and their values added, the resulting distribution is no longer uniform since not all sums have equal probability.
 
The discrete uniform distribution itself is inherently non-parametric. It is convenient, however, to represent its values generally by an integer interval ''[a,b]'', so that ''a,b'' become the main parameters of the distribution (often one simply considers the interval ''[1,n]'' with the single parameter ''n''). With these conventions, the [[cumulative distribution function]] (CDF) of the discrete uniform distribution can be expressed, for any ''k'' ∈ ''[a,b]'', as
 
:<math>F(k;a,b)=\frac{\lfloor k \rfloor -a + 1}{b-a+1}</math>
 
==Estimation of maximum==
{{main|German tank problem}}
This example is described by saying that a sample of ''k'' observations is obtained from a uniform distribution on the integers <math>1,2,\dots,N</math>, with the problem being to estimate the unknown maximum ''N''.  This problem is commonly known as the [[German tank problem]], following the application of maximum estimation to estimates of German tank production during [[World War II]].
 
The [[UMVU]] estimator for the maximum is given by
:<math>\hat{N}=\frac{k+1}{k} m - 1 = m + \frac{m}{k} - 1</math>
where ''m'' is the [[sample maximum]] and ''k'' is the [[sample size]], sampling without replacement.<ref name="Johnson">{{citation
|last=Johnson
|first=Roger
|title=Estimating the Size of a Population
|year=1994
|journal=[http://www.rsscse.org.uk/ts/index.htm Teaching Statistics]
|volume=16
|issue=2 (Summer)
|doi=10.1111/j.1467-9639.1994.tb00688.x
}}</ref><ref name="Johnson2">{{citation
|last=Johnson
|first=Roger
|contribution=Estimating the Size of a Population
|title=Getting the Best from Teaching Statistics
|year=2006
|url=http://www.rsscse.org.uk/ts/gtb/contents.html
|contribution-url=http://www.rsscse.org.uk/ts/gtb/johnson.pdf
}}</ref> This can be seen as a very simple case of [[maximum spacing estimation]].
 
The formula may be understood intuitively as:
:"The sample maximum plus the average gap between observations in the sample",
the gap being added to compensate for the negative bias of the sample maximum as an estimator for the population maximum.<ref group="notes">The sample maximum is never more than the population maximum, but can be less, hence it is a [[biased estimator]]: it will tend to ''underestimate'' the population maximum.</ref>
 
This has a variance of<ref name="Johnson"/>
:<math>\frac{1}{k}\frac{(N-k)(N+1)}{(k+2)} \approx \frac{N^2}{k^2} \text{ for small samples } k \ll N</math>
so a standard deviation of approximately <math>N/k</math>, the (population) average size of a gap between samples; compare <math>\frac{m}{k}</math> above.
 
The sample maximum is the [[maximum likelihood]] estimator for the population maximum, but, as discussed above, it is biased.
 
If samples are not numbered but are recognizable or markable, one can instead estimate population size via the [[capture-recapture]] method.
 
==Random permutation==
{{main|Random permutation}}
See [[rencontres numbers]] for an account of the probability distribution of the number of fixed points of a uniformly distributed [[random permutation]].
 
==See also==
* [[Delta distribution]]
* [[Uniform distribution (continuous)]]
 
==Notes==
{{reflist|group=notes}}
 
==References==
{{reflist}}
 
{{ProbDistributions|discrete-finite}}
{{Common univariate probability distributions}}
 
{{DEFAULTSORT:Uniform Distribution (Discrete)}}
[[Category:Discrete distributions]]
[[Category:Probability distributions]]
 
[[su:Sebaran seragam#Kasus diskrit]]

Revision as of 00:32, 26 December 2013

Template:Probability distribution

In probability theory and statistics, the discrete uniform distribution is a symmetric probability distribution whereby a finite number of values are equally likely to be observed; every one of n values has equal probability 1/n. Another way of saying "discrete uniform distribution" would be "a known, finite number of outcomes equally likely to happen".

A simple example of the discrete uniform distribution is throwing a fair Template:Dice. The possible values are 1, 2, 3, 4, 5, 6, and each time the Template:Dice is thrown the probability of a given score is 1/6. If two dice are thrown and their values added, the resulting distribution is no longer uniform since not all sums have equal probability.

The discrete uniform distribution itself is inherently non-parametric. It is convenient, however, to represent its values generally by an integer interval [a,b], so that a,b become the main parameters of the distribution (often one simply considers the interval [1,n] with the single parameter n). With these conventions, the cumulative distribution function (CDF) of the discrete uniform distribution can be expressed, for any k[a,b], as

Estimation of maximum

Mining Engineer (Excluding Oil ) Truman from Alma, loves to spend time knotting, largest property developers in singapore developers in singapore and stamp collecting. Recently had a family visit to Urnes Stave Church. This example is described by saying that a sample of k observations is obtained from a uniform distribution on the integers , with the problem being to estimate the unknown maximum N. This problem is commonly known as the German tank problem, following the application of maximum estimation to estimates of German tank production during World War II.

The UMVU estimator for the maximum is given by

where m is the sample maximum and k is the sample size, sampling without replacement.[1][2] This can be seen as a very simple case of maximum spacing estimation.

The formula may be understood intuitively as:

"The sample maximum plus the average gap between observations in the sample",

the gap being added to compensate for the negative bias of the sample maximum as an estimator for the population maximum.[notes 1]

This has a variance of[1]

so a standard deviation of approximately , the (population) average size of a gap between samples; compare above.

The sample maximum is the maximum likelihood estimator for the population maximum, but, as discussed above, it is biased.

If samples are not numbered but are recognizable or markable, one can instead estimate population size via the capture-recapture method.

Random permutation

Mining Engineer (Excluding Oil ) Truman from Alma, loves to spend time knotting, largest property developers in singapore developers in singapore and stamp collecting. Recently had a family visit to Urnes Stave Church. See rencontres numbers for an account of the probability distribution of the number of fixed points of a uniformly distributed random permutation.

See also

Notes

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References

43 year old Petroleum Engineer Harry from Deep River, usually spends time with hobbies and interests like renting movies, property developers in singapore new condominium and vehicle racing. Constantly enjoys going to destinations like Camino Real de Tierra Adentro.

55 yrs old Metal Polisher Records from Gypsumville, has interests which include owning an antique car, summoners war hack and spelunkering. Gets immense motivation from life by going to places such as Villa Adriana (Tivoli).

my web site - summoners war hack no survey ios Template:Common univariate probability distributions

su:Sebaran seragam#Kasus diskrit

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