Minor cleanup
This commit is contained in:
Ozzie Gooen
parent
92f606b09b
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---
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title: "Creating Distributions"
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title: "Distribution Creation"
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sidebar_position: 8
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---
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@ -13,8 +13,8 @@ import TabItem from "@theme/TabItem";
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## To
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`(5thPercentile: float) to (95thPercentile: float)`
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`to(5thPercentile: float, 95thPercentile: float)`
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`(5thPercentile: number) to (95thPercentile: number)`
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`to(5thPercentile: number, 95thPercentile: number)`
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The `to` function is an easy way to generate simple distributions using predicted _5th_ and _95th_ percentiles.
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@ -44,8 +44,8 @@ If both values are above zero, a `lognormal` distribution is used. If not, a `no
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### Arguments
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- `5thPercentile`: Float
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- `95thPercentile`: Float, greater than `5thPercentile`
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- `5thPercentile`: number
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- `95thPercentile`: number, greater than `5thPercentile`
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<Admonition type="tip" title="Tip">
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<p>
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@ -68,8 +68,8 @@ If both values are above zero, a `lognormal` distribution is used. If not, a `no
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## Mixture
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`mixture(...distributions: Distribution[], weights?: float[])`
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`mx(...distributions: Distribution[], weights?: float[])`
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`mixture(...distributions: Distribution[], weights?: number[])`
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`mx(...distributions: Distribution[], weights?: number[])`
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The `mixture` mixes combines multiple distributions to create a mixture. You can optionally pass in a list of proportional weights.
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@ -87,8 +87,8 @@ The `mixture` mixes combines multiple distributions to create a mixture. You can
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### Arguments
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- `distributions`: A set of distributions or floats, each passed as a paramater. Floats will be converted into Delta distributions.
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- `weights`: An optional array of floats, each representing the weight of its corresponding distribution. The weights will be re-scaled to add to `1.0`. If a weights array is provided, it must be the same length as the distribution paramaters.
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- `distributions`: A set of distributions or numbers, each passed as a paramater. Numbers will be converted into Delta distributions.
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- `weights`: An optional array of numbers, each representing the weight of its corresponding distribution. The weights will be re-scaled to add to `1.0`. If a weights array is provided, it must be the same length as the distribution paramaters.
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### Aliases
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@ -100,7 +100,7 @@ The `mixture` mixes combines multiple distributions to create a mixture. You can
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<summary>🕐 Zero or Continuous</summary>
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<p>
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One common reason to have mixtures of continous and discrete distributions is to handle the special case of 0.
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Say I want to model the time I will spend on some upcoming assignment. I think I have an 80% chance of doing it.
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Say I want to model the time I will spend on some upcoming project. I think I have an 80% chance of doing it.
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</p>
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<p>
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@ -120,10 +120,6 @@ mx(hours_the_project_will_take, 0, [chance_of_doing_anything, 1 - chance_of_doin
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"just-in-case distribution". This latter distribution would have very low weight, but would be
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very wide, just in case they were dramatically off for some weird reason.
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</p>
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<p>
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One common reason to have mixtures of continous and discrete distributions is to handle the special case of 0.
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Say I want to model the time I will spend on some upcoming assignment. I think I have an 80% chance of doing it.
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</p>
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<SquiggleEditor
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initialSquiggleString={`forecast = 3 to 30
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chance_completely_wrong = 0.05
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@ -135,7 +131,7 @@ mx(forecast, forecast_if_completely_wrong, [1-chance_completely_wrong, chance_co
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## Normal
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`normal(mean:float, standardDeviation:float)`
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`normal(mean:number, standardDeviation:number)`
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Creates a [normal distribution](https://en.wikipedia.org/wiki/Normal_distribution) with the given mean and standard deviation.
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<Tabs>
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@ -149,29 +145,28 @@ Creates a [normal distribution](https://en.wikipedia.org/wiki/Normal_distributio
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### Arguments
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- `mean`: Float
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- `standard deviation`: Float greater than zero
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- `mean`: Number
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- `standard deviation`: Number greater than zero
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[Wikipedia](https://en.wikipedia.org/wiki/Normal_distribution)
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## Log-normal
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`lognormal(mu: float, sigma: float)`
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`lognormal(mu: number, sigma: number)`
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Creates a [log-normal distribution](https://en.wikipedia.org/wiki/Log-normal_distribution) with the given mu and sigma.
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`Mu` and `sigma` can be difficult to directly reason about. Because of this complexity, we recommend typically using the <a href="#to">to</a> syntax instead of estimating `mu` and `sigma` directly.
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<SquiggleEditor initialSquiggleString="lognormal(0, 0.7)" />
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### Arguments
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- `mu`: Float
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- `sigma`: Float greater than zero
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- `mu`: Number
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- `sigma`: Number greater than zero
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[Wikipedia](https://en.wikipedia.org/wiki/Log-normal_distribution)
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### Argument Alternatives
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`Mu` and `sigma` can be difficult to directly reason about. Because of this complexity, we recommend typically using the <a href="#to">to</a> syntax.
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<details>
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<summary>❓ Understanding <bold>mu</bold> and <bold>sigma</bold></summary>
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<p>
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@ -187,15 +182,15 @@ logOfLognormal = log(lognormal(normalMean, normalStdDev))
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## Uniform
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`uniform(low:float, high:float)`
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`uniform(low:number, high:number)`
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Creates a [uniform distribution](https://en.wikipedia.org/wiki/Uniform_distribution_(continuous)) with the given low and high values.
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<SquiggleEditor initialSquiggleString="uniform(3,7)" />
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### Arguments
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- `low`: Float
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- `high`: Float greater than `low`
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- `low`: Number
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- `high`: Number greater than `low`
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<Admonition type="caution" title="Caution">
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<p>
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@ -208,7 +203,7 @@ Creates a [uniform distribution](https://en.wikipedia.org/wiki/Uniform_distribut
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</Admonition>
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## Beta
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``beta(alpha:float, beta:float)``
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``beta(alpha:number, beta:number)``
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Creates a [beta distribution](https://en.wikipedia.org/wiki/Beta_distribution) with the given `alpha` and `beta` values. For a good summary of the beta distribution, see [this explanation](https://stats.stackexchange.com/a/47782) on Stack Overflow.
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@ -232,8 +227,8 @@ Creates a [beta distribution](https://en.wikipedia.org/wiki/Beta_distribution) w
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### Arguments
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- `alpha`: Float greater than zero
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- `beta`: Float greater than zero
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- `alpha`: Number greater than zero
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- `beta`: Number greater than zero
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<Admonition type="caution" title="Caution with small numbers">
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<p>
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@ -260,39 +255,37 @@ Creates a [beta distribution](https://en.wikipedia.org/wiki/Beta_distribution) w
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## Exponential
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``exponential(rate:float)``
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``exponential(rate:number)``
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Creates an [exponential distribution](https://en.wikipedia.org/wiki/Exponential_distribution) with the given rate.
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<SquiggleEditor initialSquiggleString="exponential(4)" />
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### Arguments
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- `rate`: Float greater than zero
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- `rate`: Number greater than zero
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## Triangular distribution
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``triangular(low:float, mode:float, high:float)``
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``triangular(low:number, mode:number, high:number)``
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Creates a [triangular distribution](https://en.wikipedia.org/wiki/Triangular_distribution) with the given low, mode, and high values.
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#### Validity
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### Arguments
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- `low`: Float
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- `mode`: Float greater than `low`
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- `high`: Float greater than `mode`
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- `low`: Number
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- `mode`: Number greater than `low`
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- `high`: Number greater than `mode`
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<SquiggleEditor initialSquiggleString="triangular(1, 2, 4)" />
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## FromSamples
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``fromSamples(samples:number[])``
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Creates a sample set distribution using an array of samples.
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<SquiggleEditor initialSquiggleString="fromSamples([1,2,3,4,6,5,5,5])" />
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#### Validity
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For `fromSamples(xs)`,
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- `xs.length > 5`
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- Strictly every element of `xs` must be a number.
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### Arguments
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- `samples`: An array of at least 5 numbers.
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