probability

Code: int random = (rand() % 7 + 1) if (random == 1) { } // num 1 else if (random == 2) { } // num 2 else if (random == 3 || random == 4) { } // num 3 else if (random == 5 || random == 6) { } // num 4 else if (random == 7) { } // num 5 Basically I want each of these numbers with each of these probabilities: 1: 1/7 2: 1/7 3: 2/7 4: 2/7 5: 1/7 Will this code give me proper results? I.e. if this is run infinite times, will I get the proper frequencies? Is there a less-lengthy way of doing this? derobert Not, it's actually slightly off, due to the way rand() works. In particular, rand returns

Given a positive integer array a , the goal is to generate 5 random numbers based on the weight they have in the array. For example: a = [2,3,4,4,4,4,4,6,7,8,9] In this case the number 4 has appeared 5 times, in this case the number 4 should have the probability of 5/11 to appear. No numbers should be repeated. Given a , an array of positive integers, you'll first need to compute the frequency of each integer. For example, using bincount : >>> a = [2,3,4,4,4,4,4,4,5,6,7,8,9,4,9,2,3,6,3,1] >>> b = np.bincount(a) b tells you the frequency of each integer in a . The corresponding set of weights

I'm trying to write a function in c++ that will return true or false based on a probability given. So, for example if the probability given was 0.634 then, 63.4% of the time the function would return true. I've tried a few different things, and failed. Any help? If you'd like to do this in C++11, you can use its various random number engines, combined with the uniform_real_distribution to provide a good result. The following code demonstrates: #include <random> std::knuth_b rand_engine; // replace knuth_b with one of the engines listed below std::uniform_real_distribution<> uniform_zero_to_one

Given a mean and standard-deviation defining a normal distribution , how would you calculate the following probabilities in pure-Python (i.e. no Numpy/Scipy or other packages not in the standard library)? The probability of a random variable r where r < x or r <= x. The probability of a random variable r where r > x or r >= x. The probability of a random variable r where x > r > y. I've found some libraries, like Pgnumerics , that provide functions for calculating these, but the underlying math is unclear to me. Edit: To show this isn't homework, posted below is my working code for Python<=2.6

Is there a built-in function in scipy/numpy for getting the PMF of a Multinomial? I'm not sure if binom generalizes in the correct way, e.g. # Attempt to define multinomial with n = 10, p = [0.1, 0.1, 0.8] rv = scipy.stats.binom(10, [0.1, 0.1, 0.8]) # Score the outcome 4, 4, 2 rv.pmf([4, 4, 2]) What is the correct way to do this? thanks. There's no built-in function that I know of, and the binomial probabilities do not generalize (you need to normalise over a different set of possible outcomes, since the sum of all the counts must be n which won't be taken care of by independent binomials).