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

2015
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IEEE Transactions on Information Theory
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Consider a finite set of sources, each producing i.i.d. 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 ≤{M,N} of the observed sequences may be produced by a source from the set of sources of

doi:10.1109/tit.2014.2374157
fatcat:u264ynsd6fehhdqtnmxqk5xftq