Sampling function-based probability distributions.

Version on this page:2.1.0
LTS Haskell 20.24:2.3.1
Stackage Nightly 2023-06-04:2.3.1
Latest on Hackage:2.3.1

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MIT licensed by Jared Tobin, Marco Zocca
Maintained by, zocca.marco gmail
This version can be pinned in stack with:mwc-probability-2.1.0@sha256:fe7186892a8b1d857ae2266ff4dbaf8a4d9840d0bb267e60bdc56408d3a1cb13,1856

Module documentation for 2.1.0


Build Status Hackage Version MIT License

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.


  • Transform a distribution’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.


PRs and issues welcome.