mwc-probability

Sampling function-based probability distributions.
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README.md (1491B)

      1 # mwc-probability
      2 
      3 [![Build Status](https://secure.travis-ci.org/jtobin/mwc-probability.png)](http://travis-ci.org/jtobin/mwc-probability)
      4 [![Hackage Version](https://img.shields.io/hackage/v/mwc-probability.svg)](http://hackage.haskell.org/package/mwc-probability)
      5 [![MIT License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/jtobin/mwc-probability/blob/master/LICENSE)
      6 
      7 Sampling function-based probability distributions.
      8 
      9 A simple probability distribution type, where distributions are characterized
     10 by sampling functions.
     11 
     12 This implementation is a thin layer over `mwc-random`, which handles RNG
     13 state-passing automatically by using a `PrimMonad` like `IO` or `ST s` under
     14 the hood.
     15 
     16 Examples
     17 --------
     18 
     19 * Transform a distribution's support while leaving its density structure
     20 invariant:
     21 
     22       -- uniform over [0, 1] transformed to uniform over [1, 2]
     23       succ <$> uniform
     24 
     25 * Sequence distributions together using bind:
     26 
     27       -- a beta-binomial composite distribution
     28       beta 1 10 >>= binomial 10
     29 
     30 * Use do-notation to build complex joint distributions from composable,
     31   local conditionals:
     32 
     33       hierarchicalModel = do
     34         [c, d, e, f] <- replicateM 4 (uniformR (1, 10))
     35         a <- gamma c d
     36         b <- gamma e f
     37         p <- beta a b
     38         n <- uniformR (5, 10)
     39         binomial n p
     40 
     41 Check out the haddock-generated docs on
     42 [Hackage](https://hackage.haskell.org/package/mwc-probability) for other
     43 examples.
     44 
     45 ## Etc.
     46 
     47 PRs and issues welcome.
     48