probability

How can i generate a random number between A = 1 and B = 10 where each number has a different probability? Example: number / probability 1 - 20% 2 - 20% 3 - 10% 4 - 5% 5 - 5% ...and so on. I'm aware of some hard-coded workarounds which unfortunately are of no use with larger ranges, for example A = 1000 and B = 100000. Assume we have a Rand() method which returns a random number R, 0 < R < 1, can anyone post a code sample with a proper way of doing this ? prefferable in c# / java / actionscript. Build an array of 100 integers and populate it with 20 1's, 20 2's, 10 3's, 5 4's, 5 5's, etc. Then

Let's say I'm writing a simple luck game - each player presses Enter and the game assigns him a random number between 1-6. Just like a cube. At the end of the game, the player with the highest number wins. Now, let's say I'm a cheater. I want to write the game so player #1 (which will be me) has a probability of 90% to get six, and 2% to get each of the rest numbers (1, 2, 3, 4, 5). How can I generate a number random, and set the probability for each number? static Random random = new Random(); static int CheatToWin() { if (random.NextDouble() < 0.9) return 6; return random.Next(1, 6); }

Using R, it is trivial to calculate the quantiles for given probabilities in a sampled distribution: x <- rnorm(1000, mean=4, sd=2) quantile(x, .9) # results in 6.705755 However, I can't find an easy way to do the inverse—calculate the probability for a given quantile in the sample x . The closest I've come is to use pnorm() with the same mean and standard deviation I used when creating the sample: pnorm(5, mean=4, sd=2) # results in 0.6914625 However, because this is calculating the probability from the full normal distribution, and not the sample x , it's not entirely accurate. Is there a

I need to normalize a list of values to fit in a probability distribution, i.e. between 0.0 and 1.0. I understand how to normalize, but was curious if Python had a function to automate this. I'd like to go from: raw = [0.07, 0.14, 0.07] to normed = [0.25, 0.50, 0.25] Tony Suffolk 66 Use : norm = [float(i)/sum(raw) for i in raw] to normalize against the sum to ensure that the sum is always 1.0 (or as close to as possible). use norm = [float(i)/max(raw) for i in raw] to normalize against the maximum How long is the list you're going to normalize? def psum(it): "This function makes explicit how