Chi-SquaredFit v0.9.2 Chi2fit.Distribution View Source
Provides various distributions.
Link to this section Summary
Types
Cumulative Distribution function
A probability distribution function
Keyword list containing the CDF function and the number of parameters
Functions
The Bernoulli distribution.
Distribution for flipping coins.
Constant distribution.
Distribution simulating a dice (1..6)
Distribution simulating the dice in the GetKanban V4 simulation game.
The Erlang distribution.
The Erlang cumulative distribution function.
The exponential distribution.
Fréhet or inverse Weibull distribution.
The Fréchet distribution, also known inverse Weibull distribution.
Guesses what distribution is likely to fit the sample data
Returns the model for a name.
Nakagami distribution.
The Nakagami distribution.
The normal or Gauss distribution
The normal or Gauss cumulative distribution
The Poisson distribution.
The Skew Exponential Power cumulative distribution (Azzalini).
Uniform distribution.
Uniform distribution.
Wald or Inverse Gauss distribution.
The Wald (Inverse Gauss) cumulative distribution function.
The Weibull distribution.
The Weibull cumulative distribution function.
Link to this section Types
cdf()
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cdf() :: number :: number()
cdf() :: number :: number()
Cumulative Distribution function
distribution()
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distribution() :: ... :: term()
distribution() :: ... :: term()
A probability distribution function
model()
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model() :: [fun: cdf(), df: pos_integer()]
model() :: [fun: cdf(), df: pos_integer()]
Keyword list containing the CDF function and the number of parameters
Link to this section Functions
bernoulli(value)
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bernoulli(value :: number()) :: distribution()
bernoulli(value :: number()) :: distribution()
The Bernoulli distribution.
coin()
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coin() :: distribution()
coin() :: distribution()
Distribution for flipping coins.
constant(average)
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constant(number() | Keyword.t()) :: distribution()
constant(number() | Keyword.t()) :: distribution()
Constant distribution.
dice(avg)
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dice([] | number()) :: distribution()
dice([] | number()) :: distribution()
Distribution simulating a dice (1..6)
dice_gk4(avg)
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dice_gk4([] | number()) :: distribution()
dice_gk4([] | number()) :: distribution()
Distribution simulating the dice in the GetKanban V4 simulation game.
erlang(k, lambda)
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erlang(k :: integer(), lambda :: number()) :: distribution()
erlang(k :: integer(), lambda :: number()) :: distribution()
The Erlang distribution.
erlangCDF(k, lambda) View Source
The Erlang cumulative distribution function.
exponential(rate)
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exponential(Keyword.t()) :: distribution()
exponential(Keyword.t()) :: distribution()
The exponential distribution.
exponentialCDF(rate) View Source
frechet(scale, shape)
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frechet(scale :: number(), shape :: number()) :: distribution()
frechet(scale :: number(), shape :: number()) :: distribution()
Fréhet or inverse Weibull distribution.
frechetCDF(scale, shape) View Source
The Fréchet distribution, also known inverse Weibull distribution.
guess(sample, n \\ 100, list \\ ["exponential", "poisson", "normal", "erlang", "wald", "sep", "weibull", "frechet", "nakagami"]) View Source
Guesses what distribution is likely to fit the sample data
model(name, options \\ []) View Source
Returns the model for a name.
The kurtosis is the so-called 'excess kurtosis'.
Supported disributions:
"wald" - The Wald or Inverse Gauss distribution,
"weibull" - The Weibull distribution,
"exponential" - The exponential distribution,
"poisson" - The Poisson distribution,
"normal" - The normal or Gaussian distribution,
"fechet" - The Fréchet distribution,
"nakagami" - The Nakagami distribution,
"sep" - The Skewed Exponential Power distribution (Azzalini),
"erlang" - The Erlang distribution,
"sep0" - The Skewed Exponential Power distribution (Azzalini) with location parameter set to zero (0).
Options
Available only for the SEP distribution, see 'sepCDF/5'.
nakagami(scale, shape)
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nakagami(scale :: number(), shape :: number()) :: distribution()
nakagami(scale :: number(), shape :: number()) :: distribution()
Nakagami distribution.
nakagamiCDF(scale, shape) View Source
The Nakagami distribution.
normal(mean, sigma)
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normal(mean :: number(), sigma :: number()) :: distribution()
normal(mean :: number(), sigma :: number()) :: distribution()
The normal or Gauss distribution
normalCDF(mean, sigma) View Source
The normal or Gauss cumulative distribution
poisson(rate) View Source
The Poisson distribution.
For the implementation, see https://en.wikipedia.org/wiki/Poisson_distribution, 'Generating Poisson-distributed random variables'
poissonCDF(rate) View Source
sepCDF(a, b, lambda, alpha, options \\ []) View Source
The Skew Exponential Power cumulative distribution (Azzalini).
Options
:method
- the integration method to use, :gauss and :romberg types are supported, see below
:tolerance
- re-iterate until the tolerance is reached (only for :romberg)
:points
- the number of points to use in :gauss method
Integration methods
:gauss
- n-point Gauss rule,
:gauss2
- n-point Guass rule with tanh transformation,
:gauss3
- n-point Gauss rule with linear transformstion,
:romberg
- Romberg integration,
:romberg2
- Romberg integration with tanh transformation,
:romberg3
- Romberg integration with linear transformstion.
uniform(list)
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uniform(Keyword.t()) :: distribution()
uniform(Keyword.t()) :: distribution()
Uniform distribution.
uniform(min, max)
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uniform(min :: integer(), max :: integer()) :: distribution()
uniform(min :: integer(), max :: integer()) :: distribution()
Uniform distribution.
wald(mu, lambda)
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wald(mu :: number(), lambda :: number()) :: distribution()
wald(mu :: number(), lambda :: number()) :: distribution()
Wald or Inverse Gauss distribution.
waldCDF(mu, lambda) View Source
The Wald (Inverse Gauss) cumulative distribution function.
weibull(alpha, beta)
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weibull(number(), number() | Keyword.t()) :: distribution()
weibull(number(), number() | Keyword.t()) :: distribution()
The Weibull distribution.
weibullCDF(k, lambda) View Source
The Weibull cumulative distribution function.