mwc-probability

Sampling function-based probability distributions.

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

Version on this page:	1.2.1
LTS Haskell 23.21:	2.3.1
Stackage Nightly 2025-05-04:	2.3.1
Latest on Hackage:	2.3.1

See all snapshots mwc-probability appears in

MIT licensed by Jared Tobin

Maintained by [email protected]

This version can be pinned in stack with:mwc-probability-1.2.1@sha256:adba6c52307e0478526706144187e7c5c31768c2cda3d7f6bc07758c5056f1d1,1614

Module documentation for 1.2.1

System
- System.Random
  - System.Random.MWC
    - System.Random.MWC.Probability

Depends on 4 packages(full list with versions):

base, mwc-random, primitive, transformers

Used by 6 packages in lts-6.2(full list with versions):

declarative, flat-mcmc, hasty-hamiltonian, mcmc-types, mighty-metropolis, speedy-slice

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