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On Identifying a Massive Number of Distributions

Sara Shahi, Daniela Tuninetti, Natasha Devroye
2018 2018 IEEE International Symposium on Information Theory (ISIT)  

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2018 pre-print arXiv:1801.04593v1 fatcat:6u5gkqun4jfmvolv2oanpvx64m
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.
doi:10.1109/isit.2018.8437586 dblp:conf/isit/ShahiTD18 fatcat:af5dj2boljhe3i5ydofgik4vca
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Sara Shahi, Daniela Tuninetti, Natasha Devroye. "On Identifying a Massive Number of Distributions." 2018 IEEE International Symposium on Information Theory (ISIT) (2018) 331-335