Sampling function-based probability distributions.

Version on this page:2.0.4
LTS Haskell 13.25:2.0.4
Stackage Nightly 2019-06-12:2.0.4
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

There are no documented modules for this package.


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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