Normal Distribution: Difference between revisions

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   | name      = Normal distribution
   | name      = Normal distribution
   | type      = density
   | type      = density
   | pdf_image  = [[File:Normal Distribution PDF.svg|350px|Probability density function for the normal distribution]]<br /><small>The red curve is the ''standard normal distribution''</small>
   | pdf_image  = [[File:Probability Density Function.png|350px|Probability density function for the normal distribution]]<br /><small>The red curve is the ''standard normal distribution''</small>
   | cdf_image  = [[File:Normal Distribution CDF.svg|350px|Cumulative distribution function for the normal distribution]]
   | cdf_image  = [[File:Normal_Distribution_CDF.png|350px|Cumulative distribution function for the normal distribution]]
   | notation  = <math>\mathcal{N}(\mu,\,\sigma^2)</math>
   | notation  = [[File:Dist Normal Notation.png]]
  | parameters = {{nowrap|''μ'' ∈ '''R'''}} — mean ([[location parameter|location]])<br />{{nowrap|''σ''<sup>2</sup> > 0}} — variance (squared [[scale parameter|scale]])
   | pdf        = [[File:Dist Normal PDF.png]]
  | support    = ''x'' ∈ '''R'''
   | cdf        = [[File:Dist Normal CDF.png]]
   | pdf        = <math>\frac{1}{\sigma\sqrt{2\pi}}\, e^{-\frac{(x - \mu)^2}{2 \sigma^2}}</math>
   | quantile  = [[File:Dist Normal Quantile.png]]
   | cdf        = <math>\frac12\left[1 + \operatorname{erf}\left( \frac{x-\mu}{\sigma\sqrt{2}}\right)\right] </math>
   | mean      = µ
   | quantile  = <math>\mu+\sigma\sqrt{2}\,\operatorname{erf}^{-1}(2F-1)</math>
   | median    = µ
   | mean      = {{math|''μ''}}
   | mode      = µ
   | median    = {{math|''μ''}}
   | variance  = σ²
   | mode      = {{math|''μ''}}
   | variance  = <math>\sigma^2\,</math>
   | skewness  = 0
   | skewness  = 0
   | kurtosis  = 0 <!-- DO NOT REPLACE THIS WITH THE OLD-STYLE KURTOSIS WHICH IS 3. -->
   | kurtosis  = 0 <!-- DO NOT REPLACE THIS WITH THE OLD-STYLE KURTOSIS WHICH IS 3. -->
  | entropy    = <math>\frac12 \ln(2 \pi e \, \sigma^2)</math>
  | mgf        = <math>\exp\{ \mu t + \frac{1}{2}\sigma^2t^2 \}</math>
  | char      = <math>\exp \{ i\mu t - \frac{1}{2}\sigma^2 t^2 \}</math>
  | fisher    = <math>\begin{pmatrix}1/\sigma^2&0\\0&1/(2\sigma^4)\end{pmatrix}</math>
  | conjugate prior = Normal distribution
   }}
   }}



Latest revision as of 17:34, 27 April 2015

The normal distribution is

f(x) = (1σ ) e-(x-µ) ²2σ ²


Normal distribution
Probability density function
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The red curve is the standard normal distribution
Cumulative distribution function
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Notation
PDF
CDF
Mean µ
Median µ
Mode µ
Variance σ²
Skewness 0
Kurtosis 0


Curve Equation
CDF
PDF
Half Normal Distribution Equations


  • The parameter μ in this formula is the mean or expectation of the distribution (and also its median and mode).
  • The parameter σ is its standard deviation; its variance is therefore σ' 2. A random variable with a Gaussian distribution is said to be normally distributed and is called a normal deviate.