ghc-mwc-probability
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] 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.
MIT