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ERROR FUNCTION DISTRIBUTION ​

Phitter implementation ​

Distribution Definition

python
import phitter

distribution = phitter.continuous.ErrorFunction({"h": *})

💡 The distribution's parameters are defined equation section below

Distribution Methods and Attributes

python
## CDF, PDF, PPF receive float or numpy.ndarray.
distribution.cdf(float | numpy.ndarray) # -> float | numpy.ndarray
distribution.pdf(float | numpy.ndarray) # -> float | numpy.ndarray
distribution.ppf(float | numpy.ndarray) # -> float | numpy.ndarray
distribution.sample(int) # -> numpy.ndarray

## STATS
distribution.mean # -> float
distribution.variance # -> float
distribution.standard_deviation # -> float
distribution.skewness # -> float
distribution.kurtosis # -> float
distribution.median # -> float
distribution.mode # -> float

Equations ​

Distribution Definition

X∼ErrorFunction(h)

Distribution Domain

x∈(−∞,∞)

Parameters Domain and Constraints

h∈R+

Cumulative Distribution Function

FX(x)=Φ(2hx)

Probability Density Function

fX(x)=hπe−h2x2

Percent Point Function / Sample

FX−1(u)=Φ−1(u)2h

Parametric Centered Moments

μk′=E[Xk]=∫−∞∞xkfX(x)dx

Parametric Mean

Mean(X)=μ1′=0

Parametric Variance

Variance(X)=μ2′−μ1′2=12h2

Parametric Skewness

Skewness(X)=μ3′−3μ2′μ1′+2μ1′3(μ2′−μ1′2)1.5=0

Parametric Kurtosis

Kurtosis(X)=μ4′−4μ1′μ3′+6μ1′2μ2′−3μ1′4(μ2′−μ1′2)2=3

Parametric Median

Median(X)=0

Parametric Mode

Mode(X)=0

Additional Information and Definitions

  • h:Inverse of scale parameter
  • u:Uniform[0,1] random varible
  • Φ(x):CDF normal standard distribution

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