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README
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] 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
Check out the haddock-generated docs on Hackage for other examples.
Etc.
PRs and issues welcome.
*Note that all licence references and agreements mentioned in the mwc-probability README section above
are relevant to that project's source code only.