Sampling function-based probability distributions.

Version on this page:1.2.2
LTS Haskell 20.25:2.3.1
Stackage Nightly 2023-06-10:2.3.1
Latest on Hackage:2.3.1

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

Module documentation for 1.2.2

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] 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