Distributed Estimation of Gaussian Correlations

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Two remotely located agents, Alice and Bob, observe an unlimited number of i.i.d. samples, each of a different part of a Gaussian vector. Alice can send a fixed number of bits on average to Bob, who in turn wants to estimate the correlations between the two parts of the vector. In the case where the agents observe scalar Gaussian random variables with unknown correlation, we obtain two constructive and simple unbiased estimators whose performance coincides with a known but nonconstructive random coding result of Zhang and Berger. In the vector case, which was not treated before, we obtain a nontrivial multidimensional extension that employs the coupling between the correlations to yield better performance. We also discuss application of our technique to cases where the underlying distribution is not fully known.

Original languageEnglish
Title of host publication2018 IEEE International Symposium on Information Theory, ISIT 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Print)9781538647806
StatePublished - 15 Aug 2018
Event2018 IEEE International Symposium on Information Theory, ISIT 2018 - Vail, United States
Duration: 17 Jun 201822 Jun 2018

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8095


Conference2018 IEEE International Symposium on Information Theory, ISIT 2018
Country/TerritoryUnited States


FundersFunder number
Horizon 2020 Framework Programme639573
European Research Council
Israel Science Foundation1367/14


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