Sampling function-based probability distributions.

Version on this page:1.3.0
LTS Haskell 11.6:2.0.2
Stackage Nightly 2018-04-24:2.0.2
Latest on Hackage:2.0.2

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MIT licensed by Jared Tobin, Marco Zocca
Maintained by, zocca.marco gmail

Module documentation for 1.3.0


Build Status Hackage Version MIT License

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.


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


# Changelog

- 2.0.2 (2018-01-30)
* Add negative binomial distribution

- 2.0.1 (2018-01-30)
* Add Normal-Gamma and Pareto distributions

- 2.0.0 (2018-01-29)
* Add Laplace and Zipf-Mandelbrot distribution
* Rename `isoGauss` to `isoNormal` and `standard` to `standardNormal` to uniform naming scheme
* Divide Haddock in sections

- 1.3.0 (2016-12-04)
* Generalize a couple of samplers to use Traversable rather than lists.

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