mwc-probability: Sampling function-based probability distributions.

[ library, math, mit ] [ Propose Tags ]

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
Versions [faq] 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, 2.0.3, 2.0.4
Dependencies base (<5), mwc-random, primitive, transformers [details]
License MIT
Author Jared Tobin
Maintainer jared@jtobin.ca
Category Math
Home page http://github.com/jtobin/mwc-probability
Source repo head: git clone http://github.com/jtobin/mwc-probability.git
Uploaded by JaredTobin at Fri Feb 12 05:46:06 UTC 2016
Distributions LTSHaskell:2.0.4, NixOS:2.0.4, Stackage:2.0.4
Downloads 4073 total (276 in the last 30 days)
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Status Hackage Matrix CI
Docs available [build log]
Last success reported on 2016-02-12 [all 1 reports]

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