Duncan Murdoch's definition is _the_ only one that I know. X is Uniform on A means E phi(X) = \int_A phi(x) dx / \int_A dx, so that the probability density is equal to 1/ \int_A dx everwhere on the set A.
By the way, another way to simulate X ~ Dirichlet(A1, A2, ..., Ad) is to generate d independent gamma variables having equal rate parameter (doesn't matter, so why not 1) and shape parameters A1, A2, ..., Ad Then the vector of components divided by their sum is the desired Dirichlet: n <- 100000 d <- 3 # for three numbers that add to one ( the unit simplex in R^3) A <- rep(1, 3) # for uniform X <- matrix(0, n, d) for (k in 1:3) X[,k] <- rgamma(n, shape=A[k], rate=1) S <- X %*% rep(1, d) Y <- X/S Present example will simulate n independant 3 vectors, each having non-negative components summing to 1, and having a distribution assigning equal mass to every possible value. Changing d and the components of A will provide an arbitrary Dirichlet on the unit simplex in R^d Grant Izmirlian NCI >> Duncan Murdoch wrote "Another definition of uniform is to have equal >> density for all possible vectors; the Dirichlet distribution with >> parameters (1,1,1) would give you that. " ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
