# mwc-probability

Sampling function-based probability distributions.

http://github.com/jtobin/mwc-probability

 Version on this page: 1.2.2 LTS Haskell 18.0: 2.3.1 Stackage Nightly 2021-06-21: 2.3.1 Latest on Hackage: 2.3.1

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MIT licensed by
Maintained by
This version can be pinned in stack with:`mwc-probability-1.2.2@sha256:dc45ea9b7af75efd0dfac885b02e0612ef54c915c19903cd3b77b62750a50208,1642`

#### Module documentation for 1.2.2

• System
• System.Random
• System.Random.MWC
Depends on 4 packages(full list with versions):
Used by 6 packages in lts-7.24(full list with versions):

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 distribution's support while leaving its density structure invariant:

``````-- uniform over [0, 1] to uniform over [1, 2]
succ <\$> uniform``````

Sequence distributions together using bind:

``````-- a beta-binomial conjugate 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``````