# mwc-probability: Sampling function-based probability distributions.

Versions | 1.0.0, 1.0.1, 1.0.2, 1.0.3, 1.1.3, 1.2.0, 1.2.1, 1.2.2, 1.3.0, 2.0.0, 2.0.1, 2.0.2 |
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Change log | CHANGELOG |

Dependencies | base (>=4.8 && <6), mwc‑random (>0.13 && <0.14), primitive (>=0.6 && <1.0), transformers (>=0.5 && <1.0) [details] |

License | MIT |

Author | Jared Tobin, Marco Zocca |

Maintainer | jared@jtobin.ca, zocca.marco gmail |

Category | Math |

Home page | http://github.com/jtobin/mwc-probability |

Source repo | head: git clone http://github.com/jtobin/mwc-probability.git |

Uploaded | by ocramz at Tue Jan 30 16:48:59 UTC 2018 |

Distributions | LTSHaskell:2.0.2, NixOS:2.0.2, Stackage:2.0.2, openSUSE:1.3.0 |

Downloads | 2339 total (44 in the last 30 days) |

Rating | (no votes yet) [estimated by rule of succession] |

Your Rating | |

Status | Docs available [build log] Last success reported on 2018-01-30 [all 1 reports] Hackage Matrix CI |

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 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

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## Downloads

- mwc-probability-2.0.2.tar.gz [browse] (Cabal source package)
- Package description (as included in the package)