Proposed new feature or change: Objective: Sample elements from the given iterator (a list, a numpy array, etc.) based upon pre-defined probabilities associated with each element which may not sums upto 1. Overview * In the numpy.random.choice function the cases where we're explicitly providing the list of probabilistic values for an input sample, the requirement expects the sum of the whole list to be 1. This makes sense when all the elements are possible observation for a single random variable whose pmf(probability mass function) is nothing but the p list. * But when every element in that list can be regarded as observation of separate independent Bernoulli r.v. (random variable), then the problem falls into the category of multi-label. Intuitively speaking, for each element in the list or array given to us, we'll toss a coin (may be biased or unbiased) ~B(p) (i.e., follows Bernoulli with p as success probability for head). * The output array or list would probably be a proper subset or can be a full list or can be an empty one. * Plus, here the argument size should automatically get shut down as we just want which all elements got selected into the output list after n coin tossess (where len(list) = n). Also it may happen that each element is independent as argued above, but the sum(p) = 1. Then we can probably put an extra argument independence = True or independence = False.