probability

You have a biased random number generator that produces a 1 with a probability p and 0 with a probability (1-p). You do not know the value of p. Using this make an unbiased random number generator which produces 1 with a probability 0.5 and 0 with a probability 0.5. Note : this problem is an exercise problem from Introduction to Algorithms by Cormen, Leiserson, Rivest, Stein.(clrs) pau.estalella The events (p)(1-p) and (1-p)(p) are equiprobable. Taking them as 0 and 1 respectively and discarding the other two pairs of results you get an unbiased random generator. In code this is done as easy

I want to generate a number based on a distributed probability. For example, just say there are the following occurences of each numbers: Number| Count 1 | 150 2 | 40 3 | 15 4 | 3 with a total of (150+40+15+3) = 208 then the probability of a 1 is 150/208= 0.72 and the probability of a 2 is 40/208 = 0.192 How do I make a random number generator that returns be numbers based on this probability distribution? I'm happy for this to be based on a static, hardcoded set for now but I eventually want it to derive the probability distribution from a database query. I've seen similar examples like this

I am working on a simulation system. I will soon have experimental data (histograms) for the real-world distribution of values for several simulation inputs. When the simulation runs, I would like to be able to produce random values that match the measured distribution. I'd prefer to do this without storing the original histograms. What are some good ways of Mapping a histogram to a set of parameters representing the distribution? Generating values that based on those parameters at runtime? EDIT: The input data are event durations for several different types of events. I expect that different

Python has my_sample = random.sample(range(100), 10) to randomly sample without replacement from [0, 100) . Suppose I have sampled n such numbers and now I want to sample one more without replacement (without including any of the previously sampled n ), how to do so super efficiently? update: changed from "reasonably efficiently" to "super efficiently" (but ignoring constant factors) Chronial Note to readers from OP: Please consider looking at the originally accepted answer to understand the logic, and then understand this answer. Aaaaaand for completeness sake: This is the concept of

Say, if I wanted to generate an unbiased random number between min and max , I'd do: var rand = function(min, max) { return Math.floor(Math.random() * (max - min + 1)) + min; }; But what if I want to generate a random number between min and max but more biased towards a value N between min and max to a degree D ? It's best to illustrate it with a probability curve: Here is one way: Get a random number in the min-max range Get a random normalized mix value Mix random with bias based on random mix Ie., in pseudo: Variables: min = 0 max = 100 bias = 67 (N) influence = 1 (D) [0.0, 1.0] Formula:

I was curious to know, how do I implement probability in Java? For example, if the chances of a variable showing is 1/25, then how would I implement that? Or any other probability? Please point me in the general direction. You'd use Random to generate a random number, then test it against a literal to match the probability you're trying to achieve. So given: boolean val = new Random().nextInt(25)==0; val will have a 1/25 probability of being true (since nextInt() has an even probability of returning any number starting at 0 and up to, but not including, 25.) You would of course have to import