Sampling function-based probability distributions.

Version on this page:2.0.4
LTS Haskell 13.23:2.0.4
Stackage Nightly 2019-05-26:2.0.4
Latest on Hackage:2.0.4

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MIT licensed by Jared Tobin, Marco Zocca
Maintained by, zocca.marco gmail

Module documentation for 2.0.4


Build Status Hackage Version MIT License

Sampling function-based probability distributions.

A simple probability distribution type, where distributions are characterized by sampling functions.

This implementation is a thin layer over mwc-random, which handles RNG state-passing automatically by using a PrimMonad like IO or ST s under the hood.


  • Transform a distribution’s support while leaving its density structure invariant:

    -- uniform over [0, 1] transformed to uniform over [1, 2]
    succ <$> uniform
  • Sequence distributions together using bind:

    -- a beta-binomial composite distribution
    beta 1 10 >>= binomial 10
  • Use do-notation to build complex joint distributions from composable, local conditionals:

    hierarchicalModel = do
      [c, d, e, f] <- replicateM 4 (uniformR (1, 10))
      a <- gamma c d
      b <- gamma e f
      p <- beta a b
      n <- uniformR (5, 10)
      binomial n p

Check out the haddock-generated docs on Hackage for other examples.


PRs and issues welcome.


# Changelog

- 2.0.4 (2018-06-30)
* Clean up docs and add some additional usage information.
* Split the existing Student t distribution into 'student' and its
generalised variant, 'gstudent'.

- 2.0.3 (2018-05-09)
* Add inverse Gaussian (Wald) distribution

- 2.0.2 (2018-01-30)
* Add negative binomial distribution

- 2.0.1 (2018-01-30)
* Add Normal-Gamma and Pareto distributions

- 2.0.0 (2018-01-29)
* Add Laplace and Zipf-Mandelbrot distribution
* Rename `isoGauss` to `isoNormal` and `standard` to `standardNormal` to
uniform naming scheme.
* Divide Haddock in sections

- 1.3.0 (2016-12-04)
* Generalize a couple of samplers to use Traversable rather than lists.

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