# mwc-probability

Sampling function-based probability distributions.

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

 Version on this page: 2.2.0 LTS Haskell 22.23: 2.3.1 Stackage Nightly 2024-05-28: 2.3.1 Latest on Hackage: 2.3.1

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This version can be pinned in stack with:`mwc-probability-2.2.0@sha256:b65d590b270b76fd20ebe7731fa03084dc016982ed2c45e68913c5b24836209e,1884`

#### Module documentation for 2.2.0

• System
• System.Random
• System.Random.MWC
Depends on 5 packages(full list with versions):
Used by 6 packages in lts-15.3(full list with versions):

# 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.