WALKER_SAMPLE is a FORTRAN90 library which efficiently samples a discrete probability vector.
For outcomes labeled 1, 2, 3, ..., N, a discrete probability vector X is an array of N non-negative values which sum to 1, such that X[i] is the probability of outcome i.
To sample the probability vector is to produce a sequence of outcomes i1, i2, i3, ..., which are each drawn with probability corresponding to X. For a general discrete probability vector X, a single sample operation might be expected to take a time that is proportional to O(N), the number of outcomes. Walker showed that, by constructing a new data structure, it was possible to carry out a sample in time of order O(1), that is, independent of the number of possible outcomes.
The computer code and data files described and made available on this web page are distributed under the GNU LGPL license.
WALKER_SAMPLE is available in a C version and a C++ version and a FORTRAN90 version and a Matlab version and a Python version.
HISTOGRAM_DATA_2D_SAMPLE, a FORTRAN90 program which demonstrates how to construct a Probability Density Function (PDF) from a frequency table over a 2D domain, and then to use that PDF to create new samples.
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You can go up one level to the FORTRAN90 source codes.