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< | {{multiple issues| | ||
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{{Probability distribution| | |||
name =Van Houtum distribution| | |||
type =mass| | |||
pdf_image =[[File:Van Houtum distribution.PNG|325px|Van Houtum distribution probability mass function example]]| | |||
parameters =<math>p_a,p_b \in [0,1] \text{ and } a,b \in \mathbb{Z} \text{ with } a\leq b</math>| | |||
support =<math>k \in \{a,a+1,\dots,b-1,b\}\,</math>| | |||
pdf =<math>\begin{cases} p_a & \text{if } u=a; \\ p_b & \text{if } u=b \\ | |||
\frac{1-p_a-p_b}{b-a-1} & \text{if } a<u<b \\ 0 & \text{otherwise} \end{cases} </math>| | |||
cdf =<math> \begin{cases} 0 & \textrm{if } u<a; \\ p_a & \text{if } u=a \\ p_a+\lfloor x-a | |||
\rfloor \frac{1-p_a-p_b}{b-a-1} & \text{if } a<u<b \\ 1 & \text{if } u \geq b \end{cases} </math>| | |||
median = | | |||
mean =<math>ap_a+bp_b+(1-p_a-p_b)\frac{a+b}{2}</math>| | |||
variance =<math> \ a^2p_a+b^2p_b - {} \ </math> | |||
<math>\frac{(a+b)(1-p_a-p_b)+2ap_a+2bp_b}{4}</math> | |||
<math>{} + \frac{b(2b-1)(b-1)-a(2a+1)(a+1)}{6}</math>| | |||
mode =N/A| | |||
skewness = | | |||
kurtosis = | | |||
entropy =<math> \ -p_a \ln(p_a)-p_b\ln(p_b)- {} \ </math> | |||
<math> (1-p_a-p_b)\ln\left(\frac{1-p_a-p_b}{b-a-1}\right)</math>| | |||
mgf = <math>e^{ta}p_a+e^tbp_b+\frac{1-p_a-p_b}{b-a-1}\frac{e^{(a+1)t}-e^{bt}}{e^t-1}</math>| | |||
cf = <math>e^{ita}p_a+e^{itb}p_b+\frac{1-p_a-p_b}{b-a-1}\frac{e^{(a+1)it}-e^{bit}}{e^{it}-1}</math>| | |||
}} | |||
In [[probability theory]] and [[statistics]], the '''Van Houtum distribution''' is a [[discrete probability distribution]] named after prof. Geert-Jan van Houtum.<ref>A. Saura (2012), Van Houtumin jakauma (in Finnish). BSc Thesis, University of Helsinki, Finland</ref> It can be characterized by saying that all values of a finite set of possible values are equally probable, except for the smallest and largest element of this set. Since the Van Houtum distribution is a generalization of the [[discrete uniform distribution]], i.e. it is uniform except possibly at its boundaries, it is sometimes also referred to as '''quasi-uniform'''. | |||
It is regularly the case that the only available information concerning some discrete random variable variable are its first two moments. The Van Houtum distribution can be used to fit a distribution with finite support on these moments. | |||
A simple example of the Van Houtum distribution arises when throwing a [[loaded dice]] which has been tampered with to land on a 6 twice as often as on a 1. The possible values of the sample space are 1, 2, 3, 4, 5 and 6. Each time the die is thrown, the probability of throwing a 2, 3, 4 or 5 is 1/6; the probability of a 1 is 1/9 and the probability of throwing a 6 is 2/9. | |||
==Probability mass function== | |||
A [[random variable]] ''U'' has a Van Houtum (''a'', ''b'', ''p''<sub>''a''</sub>, ''p''<sub>''b''</sub>) distribution if its [[probability mass function]] is | |||
: <math>\Pr(U=u) = \begin{cases} p_a & \text{if } u=a; \\[8pt] | |||
p_b & \text{if } u=b \\[8pt] | |||
\dfrac{1-p_a-p_b}{b-a-1} & \text{if } a<u<b \\[8pt] | |||
0 & \text{otherwise} \end{cases} </math> | |||
==Fitting procedure== | |||
Suppose a random variable <math>X</math> has mean <math>\mu</math> and squared [[coefficient of variation]] <math>c^2</math>. Let <math>U</math> be a Van Houtum distributed random variable. Then the first two moments of <math>U</math> match the first two moments of <math>X</math> if <math>a</math>, <math>b</math>, <math>p_a</math> and <math>p_b</math> are chosen such that:<ref>J.J. Arts (2009), ''[http://alexandria.tue.nl/extra2/afstversl/tm/Arts%202009.pdf Efficient optimization of the Dual-Index policy using Markov Chain approximations]''. MSc Thesis, Eindhoven University of Technology, The Netherlands (Appendix B)</ref> | |||
: <math> | |||
\begin{align} | |||
a &= \left\lceil \mu - \frac{1}{2} \left\lceil \sqrt{1+12c^2\mu^2} \right\rceil \right\rceil \\[8pt] | |||
b &= \left\lfloor \mu + \frac{1}{2} \left\lceil \sqrt{1+12c^2\mu^2} \right\rceil \right\rfloor \\[8pt] | |||
p_b &= \frac{(c^2+1)\mu^2-A-(a^2-A)(2\mu-a-b)/(a-b)}{a^2+b^2-2A} \\[8pt] | |||
p_a &= \frac{2\mu-a-b}{a-b}+p_b \\[12pt] | |||
\text{where } A & = \frac{2a^2+a+2ab-b+2b^2}{6}. | |||
\end{align} | |||
</math> | |||
There does not exist a Van Houtum distribution for every combination of <math>\mu</math> and <math>c^2</math>. By using the fact that for any real mean <math>\mu</math> the discrete distribution on the integers that has minimal variance is concentrated on the integers <math>\lfloor \mu \rfloor</math> and <math>\lceil \mu \rceil</math>, it is easy to verify that a Van Houtum distribution (or indeed any discrete distribution on the integers) can only be fitted on the first two moments if <ref>I.J.B.F. Adan, M.J.A. van Eenige, and J.A.C. Resing. "Fitting discrete distributions on the | |||
first two moments". ''Probability in the Engineering and Informational Sciences'', 9:623-632, | |||
1996.</ref> | |||
: <math>c^2\mu^2 \geq (\mu-\lfloor \mu \rfloor)(1+\mu-\lceil \mu \rceil)^2+(\mu-\lfloor \mu \rfloor)^2(1+\mu-\lceil \mu \rceil).</math> | |||
==See also== | |||
* [[Uniform distribution (discrete)]] | |||
== References == | |||
{{reflist}} | |||
{{ProbDistributions|discrete-finite}} | |||
[[Category:Discrete distributions]] | |||
[[Category:Probability distributions]] |
Revision as of 22:53, 13 January 2013
Template:Probability distribution
In probability theory and statistics, the Van Houtum distribution is a discrete probability distribution named after prof. Geert-Jan van Houtum.[1] It can be characterized by saying that all values of a finite set of possible values are equally probable, except for the smallest and largest element of this set. Since the Van Houtum distribution is a generalization of the discrete uniform distribution, i.e. it is uniform except possibly at its boundaries, it is sometimes also referred to as quasi-uniform.
It is regularly the case that the only available information concerning some discrete random variable variable are its first two moments. The Van Houtum distribution can be used to fit a distribution with finite support on these moments.
A simple example of the Van Houtum distribution arises when throwing a loaded dice which has been tampered with to land on a 6 twice as often as on a 1. The possible values of the sample space are 1, 2, 3, 4, 5 and 6. Each time the die is thrown, the probability of throwing a 2, 3, 4 or 5 is 1/6; the probability of a 1 is 1/9 and the probability of throwing a 6 is 2/9.
Probability mass function
A random variable U has a Van Houtum (a, b, pa, pb) distribution if its probability mass function is
Fitting procedure
Suppose a random variable has mean and squared coefficient of variation . Let be a Van Houtum distributed random variable. Then the first two moments of match the first two moments of if , , and are chosen such that:[2]
There does not exist a Van Houtum distribution for every combination of and . By using the fact that for any real mean the discrete distribution on the integers that has minimal variance is concentrated on the integers and , it is easy to verify that a Van Houtum distribution (or indeed any discrete distribution on the integers) can only be fitted on the first two moments if [3]
See also
References
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- ↑ A. Saura (2012), Van Houtumin jakauma (in Finnish). BSc Thesis, University of Helsinki, Finland
- ↑ J.J. Arts (2009), Efficient optimization of the Dual-Index policy using Markov Chain approximations. MSc Thesis, Eindhoven University of Technology, The Netherlands (Appendix B)
- ↑ I.J.B.F. Adan, M.J.A. van Eenige, and J.A.C. Resing. "Fitting discrete distributions on the first two moments". Probability in the Engineering and Informational Sciences, 9:623-632, 1996.