probability

I'm writing a small article on humanly readable alternatives to Guids/UIDs, for example those used on TinyURL for the url hashes (which are often printed in magazines, so need to be short). The simple uid I'm generating is - 6 characters: either a lowercase letter (a-z) or 0-9. "According to my calculations captain", that's 6 mutually exclusive events, although calculating the probability of a clash gets a little harder than P(A or B) = P(A) + P(B), as obviously it includes numbers and from the code below, you can see it works out whether to use a number or letter using 50/50. I'm interested

I have a bunch of keys that each have an unlikeliness variable. I want to randomly choose one of these keys, yet I want it to be more unlikely for unlikely (key, values) to be chosen than a less unlikely (a more likely) object. I am wondering if you would have any suggestions, preferably an existing python module that I could use, else I will need to make it myself. I have checked out the random module; it does not seem to provide this. I have to make such choices many millions of times for 1000 different sets of objects each containing 2,455 objects. Each set will exchange objects among each

I need to generate some random booleans. However I need to be able to specify the probability of returning true . As a results doing: private Random random = new Random(); random.nextBoolean(); will not work. One possible solution would be: private Random random = new Random() public boolean getRandomBoolean(float p){ return random.nextFloat() < p; } I was wondering if there is a better or more natural way of doing this. EDIT: I guess I am asking whether there is a library class that provides a nextBoolean(float probability) method. Oliver Charlesworth I was wondering if there is a better or

According to this documentation , arc4random_uniform() is recommended over constructions like arc4random() % upper_bound as it avoids "modulo bias" when the upper bound is not a power of two. How bad is the bias? For example if I generate random numbers with an upper bound of 6, what's the difference between using arc4random with % and arc4random_uniform() ? arc4random() returns an unsigned 32-bit integer, meaning the values are between 0 and 2^32-1 = 4 294 967 295. Now, the bias results from the fact that the multiple subintervals created with modulo are not fitting exactly into the random

i've been looking around, but i'm not sure how to do it. i've found this page which, in the last paragraph, says: A simple generator for random numbers taken from a Poisson distribution is obtained using this simple recipe: if x 1 , x 2 , ... is a sequence of random numbers with uniform distribution between zero and one, k is the first integer for which the product x 1 · x 2 · ... · x k+1 < e -λ i've found another page describing how to generate binomial numbers, but i think it is using an approximation of poisson generation, which doesn't help me. For example, consider binomial random numbers

I'm a bit confused about how to generate integer values with probabilities. As an example, I have four integers with their probability values: 1|0.4, 2|0.3, 3|0.2, 4|0.1 How can I generate these four numbers taking into account their probabilities? Here's a useful trick :-) function randomWithProbability() { var notRandomNumbers = [1, 1, 1, 1, 2, 2, 2, 3, 3, 4]; var idx = Math.floor(Math.random() * notRandomNumbers.length); return notRandomNumbers[idx]; } A simple naive approach can be: function getRandom(){ var num=Math.random(); if(num < 0.3) return 1; //probability 0.3 else if(num < 0.6)