Asymptotically Optimal Matching of Multiple Sequences to Source Distributions and Training Sequences

Consider a finite set of sources, each producing independent identically distributed observations that follow a unique probability distribution on a finite alphabet. We study the problem of matching a finite set of observed sequences to the set of sources under the constraint that the observed sequences are produced by distinct sources. In general, the number of sequences N may be different from the number of sources M, and only some K