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| {{Probability distribution
| | Alyson is the name people use to contact me and I think it seems quite great when you say it. My working day job is an invoicing officer but I've already applied for another one. Doing ballet is some thing she would never give up. My wife and I live in Kentucky.<br><br>my blog ... [http://appin.co.kr/board_Zqtv22/688025 real psychic] |
| | name =
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| | type = density
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| | pdf_image = [[Image:Folded normal pdf.svg|350px|Probability density function for the folded-normal distribution]]<br><math>\mu=1,\sigma=1</math>
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| | cdf_image = [[Image:Folded normal cdf.svg|350px|Cumulative distribution function for the normal distribution]]<br><math>\mu=1,\sigma=1</math>
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| | notation =
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| | parameters = {{nowrap|''μ'' ∈ '''R'''}} — ([[location parameter|location]])<br />{{nowrap|''σ''<sup>2</sup> > 0}} — ([[scale parameter|scale]])
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| | support = {{nowrap|''x'' ∈ [0,<math>\infty</math>)}}
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| | pdf = (see article)
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| | cdf = (see article)
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| | mean = (see article)
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| | median =
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| | mode =
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| | variance = (see article)
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| | skewness =
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| | kurtosis = <!-- DO NOT REPLACE THIS WITH THE OLD-STYLE KURTOSIS -->
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| | entropy =
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| | mgf =
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| | char =
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| | fisher =
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| }}
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| The '''folded normal distribution''' is a [[probability distribution]] related to the [[normal distribution]]. Given a normally distributed random variable X with [[mean]] μ and [[variance]] σ<sup>2</sup>, the [[random variable]] ''Y'' = |''X''| has a folded normal distribution. Such a case may be encountered if only the magnitude of some variable is recorded, but not its sign. The distribution is called Folded because probability mass to the left of the ''x'' = 0 is "folded" over by taking the [[absolute value]].
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| The [[probability density function]] (PDF) is given by
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| :<math>f(x;\mu,\sigma)=
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| \frac{1}{\sigma\sqrt{2\pi}} \, \exp \left( -\frac{(-x-\mu)^2}{2\sigma^2} \right)
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| + \frac{1}{\sigma\sqrt{2\pi}} \, \exp \left( -\frac{(x-\mu)^2}{2\sigma^2} \right)\qquad(x \ge 0\,)</math>
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| The [[cumulative distribution function]] (CDF) is given by
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| : <math>F_Y(y; \mu, \sigma) = \int_0^y \frac{1}{\sigma\sqrt{2\pi}} \, \exp \left( -\frac{(-x-\mu)^2}{2\sigma^2} \right)\, dx
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| + \int_0^{y} \frac{1}{\sigma\sqrt{2\pi}} \, \exp \left( -\frac{(x-\mu)^2}{2\sigma^2} \right)\, dx.</math>
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| Using the change-of-variables z = (''x'' − μ)/σ, the CDF can be written as
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| : <math>F_Y(y; \mu, \sigma) = \int_{-\mu/\sigma}^{(y-\mu)/\sigma} \frac{1}{\sqrt{2\pi}} \, \exp \left(-\frac{1}{2}\left(z + \frac{2\mu}{\sigma}\right)^2\right) dz
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| + \int_{-\mu/\sigma}^{(y-\mu)/\sigma} \frac{1}{\sqrt{2\pi}} \, \exp \left( -\frac{z^2}{2} \right) dz.
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| </math>
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| Alternatively, using the change of variables <math>z = -(x+\mu)/\sqrt{2}\sigma</math> in the first integral and <math>z = (x-\mu)/\sqrt{2}\sigma</math> in the second integral, one can show that
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| : <math>F_Y(y; \mu, \sigma) = \frac{1}{2}\left[ \mbox{erf}\left(\frac{y+\mu}{\sqrt{2}\sigma}\right) + \mbox{erf}\left(\frac{y-\mu}{\sqrt{2}\sigma}\right)\right],
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| </math>
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| where erf(x) is the [[error function]], which is a standard function in many mathematical software packages. This expression reduces to the CDF of the half-normal distribution when μ = 0.
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| The expectation is then given by
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| : <math>E(y) = \sigma \sqrt{2/\pi} \exp(-\mu^2/2\sigma^2) + \mu\left[1-2\Phi(-\mu/\sigma)\right],</math>
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| where Φ(•) denotes the cumulative distribution function of a standard normal distribution.
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| The variance is given by
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| : <math>\operatorname{Var}(y) = \mu^2 + \sigma^2 - \left\{ \sigma \sqrt{2/\pi} \exp(-\mu^2/2\sigma^2) + \mu\left[1-2\Phi(-\mu/\sigma)\right] \right\}^2. </math>
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| Both the mean, μ, and the variance, σ<sup>2</sup>, of ''X'' can be seen as the location and scale parameters of the new distribution.
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| == Related distributions ==
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| * When μ = 0, the distribution of ''Y'' is a [[half-normal distribution]].
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| * <math>(Y/\sigma)^2</math> has a [[noncentral chi-squared distribution]] with 1 degree of freedom and noncentrality equal to <math> (\mu/\sigma )^2</math>.
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| == See also ==
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| * [[Folded cumulative distribution]]
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| == References==
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| * {{cite journal | author=Leone FC, Nottingham RB, Nelson LS | year = 1961
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| | title = The Folded Normal Distribution
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| | journal = Technometrics | volume = 3 | issue = 4 | pages = 543–550
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| | doi = 10.2307/1266560 | jstor=1266560 | publisher=Technometrics, Vol. 3, No. 4
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| }}
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| * {{cite journal | author=Johnson NL | year = 1962
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| | title = The folded normal distribution: accuracy of the estimation by maximum likelihood
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| | journal = Technometrics | volume = 4 | issue = 2 | pages = 249–256
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| | doi = 10.2307/1266622 | jstor=1266622 | publisher=Technometrics, Vol. 4, No. 2
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| }}
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| * {{cite journal | author=Nelson LS | year = 1980
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| | title = The Folded Normal Distribution
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| | journal = J Qual Technol | volume = 12 | issue = 4 | pages = 236–238
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| | doi =
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| }}
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| * {{cite journal | author=Elandt RC | year = 1961
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| | title = The folded normal distribution: two methods of estimating parameters from moments
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| | journal = Technometrics | volume = 3 | issue = 4 | pages = 551–562
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| | doi = 10.2307/1266561 | jstor=1266561 | publisher=Technometrics, Vol. 3, No. 4
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| }}
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| * {{cite journal | author=Lin PC | year = 2005
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| | title = Application of the generalized folded-normal distribution to the process capability measures
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| | journal = Int J Adv Manuf Technol | volume = 26 | pages = 825–830
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| | doi = 10.1007/s00170-003-2043-x | issue=7–8
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| }}
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| {{ProbDistributions|continuous-semi-infinite}}
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| [[Category:Probability distributions]]
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| [[Category:Continuous distributions]]
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| [[Category:Normal distribution]]
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Alyson is the name people use to contact me and I think it seems quite great when you say it. My working day job is an invoicing officer but I've already applied for another one. Doing ballet is some thing she would never give up. My wife and I live in Kentucky.
my blog ... real psychic