Abstract-THIS PAPER IS ELIGIBLE FOR THE STUDENT PAPER AWARD. Finding the underlying probability distributions of a set of observed sequences under the constraint that each sequence is generated i.i.d by a distinct distribution is considered. The number of distributions, and hence the number of observed sequences, are let to grow with the observation blocklength n. Asymptotically matching upper and lower bounds on the probability of error are derived.
The identification of the underlying distributions of a set of observed sequences (where each sequence is generated i.i.d. by a distinct distribution) was analyzed in REF .
On Identifying a Massive Number of Distributions
2018 IEEE International Symposium on Information Theory (ISIT)
2018 IEEE International Symposium on Information Theory (ISIT)
Mathematics Computer Science