mwc-probability

Sampling function-based probability distributions.

http://github.com/jtobin/mwc-probability

Version on this page:2.0.3
LTS Haskell 22.14:2.3.1
Stackage Nightly 2024-03-28:2.3.1
Latest on Hackage:2.3.1

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MIT licensed by Jared Tobin, Marco Zocca
Maintained by [email protected], zocca.marco gmail
This version can be pinned in stack with:mwc-probability-2.0.3@sha256:3055ebb535a3f6ca15f3cec90fece674d2257a90514d939cef239450074789ff,1842

Module documentation for 2.0.3

  • System
    • System.Random
      • System.Random.MWC
        • System.Random.MWC.Probability

mwc-probability

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.

Examples

  • Transform a distribution’s support while leaving its density structure invariant:

    -- uniform over [0, 1] transformed 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
    

Included probability distributions

  • Continuous

    • Uniform
    • Normal
    • Log-Normal
    • Exponential
    • Inverse Gaussian
    • Laplace
    • Gamma
    • Inverse Gamma
    • Weibull
    • Chi-squared
    • Beta
    • Student t
    • Pareto
    • Dirichlet process
    • Symmetric Dirichlet process
  • Discrete

    • Discrete uniform
    • Zipf-Mandelbrot
    • Categorical
    • Bernoulli
    • Binomial
    • Negative Binomial
    • Multinomial
    • Poisson