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