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On Identifying a Massive Number of Distributions

journal contribution
posted on 2022-06-08, 19:06 authored by Sara Shahi, Daniela TuninettiDaniela Tuninetti, Natasha DevroyeNatasha Devroye
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.

Funding

Network Capacity When Some Common Information Theoretic Assumptions Break Down | Funder: National Science Foundation | Grant ID: CCF-1422511

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Citation

Shahi, S., Tuninetti, D.Devroye, N. (2018). On Identifying a Massive Number of Distributions. CoRR, 00, 331-335. https://doi.org/10.1109/isit.2018.8437586

Publisher

IEEE

issn

2157-8095

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