Lemming schlepptop at henning-thielemann.de
Sun May 1 08:10:11 EDT 2005

```When working with QuickCheck (the framework for tests with randomised
input) I encountered some interesting connections between random
variables in stochastics and their implementation in Haskell.

I see the following correspondence between notions in programming and in
mathematics:

random generator               ~   random variable /
probabilistic experiment
result of a random generator   ~   outcome of a probabilistic experiment

Thus the signature
rx :: (Random a, RandomGen g) => State g a
can be considered as "rx is a random variable". In the do-notation the line
x <- rx
means that "x is an outcome of rx".
In a language without higher order functions and using a random
generator "function" it is not possible to work with random variables it
is only possible to compute with outcomes, e.g. rand()+rand(). In a
language where random generators are implemented as objects computing
with random variables is possible but still cumbersome.
In Haskell we have both options either computing with outcomes
do x <- rx
y <- ry
return (x+y)
or computing with random variables
liftM2 (+) rx ry
This means that liftM like functions convert ordinary arithmetic into
random variable arithmetic. But there is also some arithmetic on random
variables which can not be performed on outcomes. For example, given a
function that repeats an action until the result fulfills a certain
property (I wonder if there is already something of this kind in the
standard libraries)

untilM :: Monad m => (a -> Bool) -> m a -> m a
untilM p m =
do x <- m
if p x then return x else untilM p m

we can suppress certain outcomes of an experiment. E.g. if
State (randomR (-10,10))
is a uniformly distributed random variable between -10 and 10, then
untilM (0/=) (State (randomR (-10,10)))
is a random variable with a uniform distribution of {-10, ..., -1, 1,
..., 10}.
```