On 13 Sep 2006, at 1:01 AM, [EMAIL PROTECTED] wrote: > Date: 12 Sep 2006 20:17:47 -0700 > From: Paul Rubin <http://[EMAIL PROTECTED]> > Subject: Re: Random Drawing Simulation -- performance issue > To: python-list@python.org > > "Travis E. Oliphant" <[EMAIL PROTECTED]> writes: >>> I need to simulate scenarios like the following: "You have a deck of >>> 3 orange cards, 5 yellow cards, and 2 blue cards. You draw a card, >>> replace it, and repeat N times." >>> >> Thinking about the problem as drawing sample froms a discrete >> distribution defined by the population might help. > > Is there some important reason you want to do this as a simulation? > And is the real problem more complicated? If you draw from the > distribution 100,000 times with replacement and sum the results, per > the Central Limit Theorem you'll get something very close to a normal > distribution whose parameters you can determine analytically. There > is probably also some statistics formula to find the precise error. > So you can replace the 100,000 draws with a single draw.
The real problem is not substantially more complicated. (The real code is, because it's embedded in a bunch of other stuff, but that's not the point.) I guess the essential reason that I want to do it as a simulation, and not as a statistics formula, is that I'd like the code to be readable (and modifiable) by a programmer who doesn't have a statistics background. I could dredge up enough of my college stats to do as you suggest (although I might not enjoy it), but I don't think I want to make that a requirement. On the other hand (quote somewhat snipped): > Date: Tue, 12 Sep 2006 22:46:04 -0500 > From: Robert Kern <[EMAIL PROTECTED]> > Subject: Re: Random Drawing Simulation -- performance issue > To: python-list@python.org > > Along the lines of what you're trying to get at, the problem that > the OP is > describing is one of sampling from a multinomial distribution. > > numpy has a function that will do the sampling for you: > > In [4]: numpy.random.multinomial? > Docstring: > Multinomial distribution. > > multinomial(n, pvals, size=None) -> random values > > pvals is a sequence of probabilities that should sum to 1 > (however, the > last element is always assumed to account for the remaining > probability > as long as sum(pvals[:-1]) <= 1). Here, I'm torn. I do want the code to be accessible to non-stats people, but this just might do the trick. Must ponder. Thanks, everyone, for your helpful suggestions! B. -- Brendon Towle, PhD Cognitive Scientist +1-412-690-2442x127 Carnegie Learning, Inc. The Cognitive Tutor Company ® Helping over 375,000 students in 1000 school districts succeed in math. -- http://mail.python.org/mailman/listinfo/python-list
