probability

I have this code snippet: Random rand = new Random(); int chance = rand.Next(1, 101); if (chance <= 25) // probability of 25% { Console.WriteLine("You win"); } else { Console.WriteLine("You lose"); } My question is, does it really calculate a 25% probability for winning here? Is the chance of winning for the player here is really 25%? Edit: I just wrote this: double total = 0; double prob = 0; Random rnd = new Random(); for (int i = 0; i < 100; i++) { double chance = rnd.Next(1, 101); if (chance <= 25) prob++; total++; } Console.WriteLine(prob / total); Console.ReadKey(); And it's highly

So I'm testing an calculating the probabilities of certain dice rolls, for a game. The base case if that rolling one 10sided die. I did a million samples of this, and ended up with the following proportions: Result 0 0.000000000000000% 1 10.038789961210000% 2 10.043589956410000% 3 9.994890005110000% 4 10.025289974710000% 5 9.948090051909950% 6 9.965590034409970% 7 9.990190009809990% 8 9.985490014509990% 9 9.980390019609980% 10 10.027589972410000% These should of course all be 10%. There is a standard deviation of 0.0323207% in these results. that, to me, seems rather high. Is it just

A cow is standing in front of an infinite fence . On the other side is grass. The cow wants to get to this grass. Somewhere along this fence is a hole through which the cow can get to the other side. The distance d from the cow to the hole has a probability distribution f(d) associated with it i.e. the probability that the hole is k steps away from the cow is given by f(k). Note that we think of all distances as discrete i.e. they are always measured in terms of steps taken by the cow.The cow can take negative integer steps as well as positive integer steps, i.e. k steps to the left and steps

Just curious but what is the probability of matching a Guid? Say a Guid from SQL server: 5AC7E650-CFC3-4534-803C-E7E5BBE29B3D is it a factorial?: (36*32)! = (1152)! discuss =D jason It's not clear what you're asking. I see two ways to interpret your question. Given a GUID g , what is the probability of someone guessing it? Let's assume for simplicity that all 128 bits of a GUID are available. Then the probability of guessing g is 2^-128 . That's small. Let's get some intuition around that. Let's assume that our attacker can generate one billion GUIDs per second. To have a 50% chance of

Suppose I have a list, called elements , each of which does or does not satisfy some boolean property p . I want to choose one of the elements that satisfies p by random with uniform distribution. I do not know ahead of time how many items satisfy this property p . Will the following code do this?: pickRandElement(elements, p) randElement = null count = 0 foreach element in elements if (p(element)) count = count + 1 if (randInt(count) == 0) randElement = element return randElement ( randInt(n) returns a random int k with 0 <= k < n .) It works mathematically. Can be proven by induction.

I'm trying to build the transition matrix for a Markov Chain in MATLAB; I have several different observation sequences (all of varying lengths) and I need to generate the transition matrix using those. Constructing a multi-order Markov chain transition matrix in Matlab shows me how to build a transition matrix with a single observation sequence. How can I construct one using observations of different length? One example can be that one sequence is 1,2,3,4 and another is 4,5,6. Is there any way to do this without having to for loop through all sequences and computing counts? So for Markov

Lets say we have some discrete distribution with finite number of possible results, is it possible to generate a random number from this distribution faster than in O(logn), where n is number possible results? How to make it in O(logn): - Make an array with cumulative probability (Array[i] = Probability that random number will be less or equal to i) - Generate random number from uniform distribution (lets denote it by k) - Find the smallest i such that k < Array[i]. It can be done using binary search. - i is our random number. Tom Minka Walker's alias method can draw a sample in constant worst

This is related to another question: Plot weighted frequency matrix . I have this graphic (produced by the code below in R): #Set the number of bets and number of trials and % lines numbet <- 36 numtri <- 1000 #Fill a matrix where the rows are the cumulative bets and the columns are the trials xcum <- matrix(NA, nrow=numbet, ncol=numtri) for (i in 1:numtri) { x <- sample(c(0,1), numbet, prob=c(5/6,1/6), replace = TRUE) xcum[,i] <- cumsum(x)/(1:numbet) } #Plot the trials as transparent lines so you can see the build up matplot(xcum, type="l", xlab="Number of Trials", ylab="Relative Frequency",

For a project I have a specification with formulas, I have to implement. In these formulas a cumulative standard normal distribution function exists, that takes a float and outputs a probability. The function is symbolized by a Φ. Exists a Java-library, that computes this function? Yuval Adam Apache Commons - Math has what you are looking for. More specifically, check out the NormalDistribution class. If you want the exact code, this one seems to be the same function used in OpenOffice Calc (I've made some changes for it to work in java): // returns the cumulative normal distribution function