Luke Palmer
Random monad is a very natural choice for "random cloud" computations. Don't think of it as a state monad -- that's an implementation detail. You can think of a value of type Random a as a "probability distribution of a's"; and there are natural definitions for the monad operators on this semantics.
I wonder if pure values and values in the Random monad as belonging to different complexity classes? It's been to long since theory of computation, but I think the relationship between BPP and the other classes is a bit unclear. (Of course, Random isn't limited to polynomial.)
I blogged about this a while ago: http://lukepalmer.wordpress.com/2009/01/17/use-monadrandom/
Nice. I've been busy writing a simulator, and thus needed to model some of the statistical distributions. The way I did this, was to say data Distribution = Uniform low high | Normal mu sigma | StudentT ... and sample :: Distribution -> Random Double sample (Normal mu sigma) = ... one reason to go beyond simply: normal :: Double -> Double -> Random Double -- normal mu sigma = sample (Normal mu sigma) is that you might want to do other things with a probability distribution than sampling it, like querying p-values for a given sample. I haven't gotten around to it yet, but I think I can get away by defining functions for the cumulative distribution and its inverse, and just write a generic function for 'sample' (which would need to pull a random probability (0<=p<=1). (But it might turn out it is useful to specialize it for simple distributions anyway?) -k -- If I haven't seen further, it is by standing in the footprints of giants