# mwc-probability: Sampling function-based probability distributions.

[ library, math, mit ] [ Propose Tags ]

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
Versions [faq] 1.0.0, 1.0.1, 1.0.2, 1.0.3, 1.1.3, 1.2.0, 1.2.1, 1.2.2, 1.3.0, 2.0.0, 2.0.1, 2.0.2, 2.0.3, 2.0.4 base (>=4.8 && <5), mwc-random, primitive, transformers [details] MIT Jared Tobin jared@jtobin.ca Revision 1 made by HerbertValerioRiedel at Thu Jul 20 14:49:23 UTC 2017 Math http://github.com/jtobin/mwc-probability head: git clone http://github.com/jtobin/mwc-probability.git by JaredTobin at Thu Mar 3 20:38:41 UTC 2016 LTSHaskell:2.0.4, NixOS:2.0.4, Stackage:2.0.4 3774 total (38 in the last 30 days) (no votes yet) [estimated by rule of succession] λ λ λ Docs available Last success reported on 2016-03-03

[Index]

## Downloads

Note: This package has metadata revisions in the cabal description newer than included in the tarball. To unpack the package including the revisions, use 'cabal get'.

#### Maintainer's Corner

For package maintainers and hackage trustees