probability

This picture from Wikipedia has a nice example of the sort of functions I'd ideally like to generate: Right now I'm using the Irwin-Hall Distribution, which is more or less a polynomial approximation of the Gaussian distribution...basically, you use uniform random number generator and iterate it x times, and take the average. The more iterations, the more like a Gaussian Distribution it is. It's pretty nice; however I'd like to be able to have one where I can vary the mean. For example, let's say I wanted a number between the range 0 and 10, but around 7. Like, the mean (if I repeated this

Does anyone know of an algorithm or data structure relating to selecting items, with a probability of them being selected proportional to some attached value? In other words: http://en.wikipedia.org/wiki/Sampling_%28statistics%29#Probability_proportional_to_size_sampling The context here is a decentralized reputation system and the attached value is therefore the value of trust one user has in another. In this system all nodes either start as friends which are completely trusted or unknowns which are completely untrusted. This isn't useful by itself in a large P2P network because there will be

As an example, here is a way to get a matrix of all possible outcomes of rolling 4 (fair) dice. z <- as.matrix(expand.grid(c(1:6),c(1:6),c(1:6),c(1:6))) As you may already have understood, I'm trying to work out a question that was closed , though, in my opinion, it's a challenging one. I used counting techniques to solve it (I mean by hand) and I finaly arrived to a number of outcomes, with a sum of subset being 5, equal to 1083 out of 1296. That result is consistent with the answers provided to that question, before it was closed. I was wondering how could that subset of outcomes (say z1,

Nothing too complicated, basically I just want to pick an element from the array as if I were making coin tosses for each index and and choosing the index when I first get a head. Also no heads means I choose the last bin. I came up with the following and was wondering if there was a better/more efficient way of doing this. def coin_toss(size) random_number = rand(2**size) if random_number == 0 return size-1 else return (0..size-1).detect { |n| random_number[n] == 1 } end end First guess...pick a random number between 1 and 2**size , find the log base 2 of that, and pick the number that many

When testing the naive classifier in matlab I get different results even though I trained and tested on the same sample data, I was wondering if my code is correct and if someone could help explain why this is? %% dimensionality reduction columns = 6 [U,S,V]=svds(fulldata,columns); %% randomly select dataset rows = 1000; columns = 6; %# pick random rows indX = randperm( size(fulldata,1) ); indX = indX(1:rows)'; %# pick random columns %indY = randperm( size(fulldata,2) ); indY = indY(1:columns); %# filter data data = U(indX,indY); %% apply normalization method to every cell data = zscore(data);