have you looked at pfp, the haskell "probabilistic functional programming
library "?
http://web.engr.oregonstate.edu/~erwig/pfp/
the paper
http://web.engr.oregonstate.edu/~erwig/papers/abstracts.html#JFP06a
describes modeling various statisticy things this way, like tree growth
and the monty hall problem, I think it's likely this is applicable to
monte carlo processes as well.
thomas.
Paul Johnson
There's a problem I've been struggling with for a long time...
I need to build a function buildSample :: [A] -> State StdGen [(A,B,C)]
given lookup functions f :: A -> [B] g :: A -> [C]
The idea is to first draw randomly form the [A], then apply each lookup function and draw randomly from the result of each.
I don't understand why this returns a list of triples instead of a single triple. Your description below seems to imply the latter. You should probably look at the "Gen" monad in Test.QuickCheck, which is basically a nice implementation of what you are doing with "State StdGen" below. Its "elements" function gets a single random element, and you can combine it with replicateM to get a list of defined length. (BTW, are you sure want multiple random samples rather than a shuffle? A shuffle has each element exactly once whereas multiple random samples can pick any element an arbitrary number of times. I ask because shuffles are a more common requirement. For the code below I'll assume you meant what you said.) Using Test.QuickCheck I think you want something like this (which I have not tested): buildSample :: [A] -> Gen (A,B,C) buildSample xs = do x <- elements xs f1 <- elements $ f x g1 <- elements $ g x return If you want n such samples then I would suggest samples <- replicateM n $ buildSample xs
It's actually slightly more complicated than this, since for the real problem I start with type [[A]], and want to map buildSample over these, and sample from the results.
There seem to be so many ways to deal with random numbers in Haskell.
Indeed.
After some false starts, I ended up doing something like
sample :: [a] -> State StdGen [a] sample [] = return [] sample xs = do g <- get let (g', g'') = split g bds = (1, length xs) xArr = listArray bds xs put g'' return . map (xArr !) $ randomRs bds g'
Not bad, although you could instead have a sample function that returns a single element and then use replicateM to get a list.
buildSample xs = sample $ do x <- xs y <- f x z <- g x return (x,y,z)
This is really bad, since it builds a huge array of all the possibilities and then draws from that. Memory is way leaky right now. I'd like to be able to just have it apply the lookup functions as needed.
Also, I'm still using GHC 6.6, so I don't have Control.Monad.State.Strict. Not sure how much difference this makes, but I guess I could just copy the source for that module if I need to.
Strictness won't help. In fact you would be better with laziness if that were possible (which it isn't here). The entire array has to be constructed before you can look up any elements in it. That forces the entire computation. But compare your implementation of buildSample to mine. Paul. _______________________________________________ Haskell-Cafe mailing list Haskell-Cafe@haskell.org http://www.haskell.org/mailman/listinfo/haskell-cafe --- This e-mail may contain confidential and/or privileged information. If you are not the intended recipient (or have received this e-mail in error) please notify the sender immediately and destroy this e-mail. Any unauthorized copying, disclosure or distribution of the material in this e-mail is strictly forbidden.