mwc-probability-2.0.0: README.md

# mwc-probability

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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 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 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