https://github.com/ocramz/splitmix-distributions

Sampling procedures for some common random variables based on splitmix
https://github.com/ocramz/splitmix-distributions

random-generation random-sampling splitmix statistics

Last synced: 22 days ago
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Sampling procedures for some common random variables based on splitmix

• Host: GitHub
• URL: https://github.com/ocramz/splitmix-distributions
• Owner: ocramz
• License: bsd-3-clause
• Created: 2021-05-03T08:21:11.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2022-05-05T05:47:22.000Z (over 2 years ago)
• Last Synced: 2024-05-01T23:26:09.609Z (6 months ago)
• Topics: random-generation, random-sampling, splitmix, statistics
• Language: Haskell
• Homepage:
• Size: 22.5 KB
• Stars: 2
• Watchers: 3
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Changelog: ChangeLog.md
  • License: LICENSE

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README

        
# splitmix-distributions

Random samplers for some common distributions, as well as a convenient interface for composing them, based on `splitmix`.

## Usage

Compose your random sampler out of simpler ones thanks to the Applicative and Monad interface, e.g. this is how you would declare and sample a binary mixture of Gaussian random variables:

    import Control.Monad (replicateM)

    import System.Random.SplitMix.Distributions (Gen, sample, bernoulli, normal)

    process :: Gen Double

    process = do

        coin <- bernoulli 0.7

        if coin

        then

            normal 0 2

        else

            normal 3 1

    dataset :: [Double]

    dataset = sample 1234 $ replicateM 20 process

and sample your data in a pure (`sample`) or monadic (`sampleT`) setting.

## Implementation details

The library is built on top of `splitmix`, so the caveats on safety and performance that apply there are relevant here as well.