# mwc-probability

Sampling function-based probability distributions. http://github.com/jtobin/mwc-probability

 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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Maintained by

#### Module documentation for 2.0.4

• System
• System.Random
• System.Random.MWC

# mwc-probability

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.

## Examples

• Transform a distributionâ€™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.

## Etc.

PRs and issues welcome.

## Changes

# Changelog

- 2.0.4 (2018-06-30)
* 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)

- 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.

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

Depends on 4 packages:
Used by 8 packages: