I’m looking for something like torch.distributions.Multinomial().log_prob(), that only allows for 1 sample from each category.
I know you can do weighted random sampling without replacement using torch.multinomial, and I believe that is sampling from the categorical distribution. I’m trying to figure out how to compute the log_prob of such an unordered weighted sample? (Not sure if I compute it as multiple independent samples from a categorical distribution, or if it should be modeled as a conditional bernoulli distribution, or hypergeometric etc.)
This is perhaps more of a statistics theory question, but just wanted to check it’s not already implemented before I go and try to do it myself.