homepage: http://github.com/jtobin/mwc-probability
changelog-type: ''
hash: 153e50fbe3ea06e256d6546690b8d41360a62d538db6cd3554a3129e33cac42a
test-bench-deps: {}
maintainer: jared@jtobin.ca
synopsis: Sampling function-based probability distributions.
changelog: ''
basic-deps:
  mwc-random: -any
  base: <5
  transformers: -any
  primitive: -any
all-versions:
- '1.0.0'
author: Jared Tobin
latest: '1.0.0'
description-type: haddock
description: ! '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.


  Includes Functor, Applicative, Monad, and MonadTrans instances.


  /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'
license-name: MIT

Update from Hackage at 2015-10-06T07:37:27+0000
Update from Hackage at 2015-10-06T07:37:27+0000
