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CAUCHY DISTRIBUTION ​

Phitter implementation ​

Distribution Definition

python
import phitter

distribution = phitter.continuous.Cauchy({"x0": *, "gamma": *})

💡 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∼Cauchy(x0,γ)

Distribution Domain

x∈(−∞,+∞)

Parameters Domain and Constraints

x0∈R,γ∈R+

Cumulative Distribution Function

FX(x)=1πarctan⁡(x−x0γ)+12

Probability Density Function

fX(x)=1πγ[1+(x−x0γ)2]

Percent Point Function / Sample

FX−1(u)=x0+γtan⁡[π(u−12)]

Parametric Centered Moments

μk′=E[Xk]=∫−∞∞xkfX(x)dx

Parametric Mean

Mean(X)=undefined

Parametric Variance

Variance(X)=undefined

Parametric Skewness

Skewness(X)=undefined

Parametric Kurtosis

Kurtosis(X)=undefined

Parametric Median

Median(X)=x0

Parametric Mode

Mode(X)=x0

Additional Information and Definitions

  • x0:Location parameter
  • γ:Scale parameter
  • u:Uniform[0,1] random varible

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