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dc.contributor.authorWeinberger, Nir
dc.contributor.authorShayevitz, Ofer
dc.date.accessioned2020-04-27T17:27:46Z
dc.date.available2020-04-27T17:27:46Z
dc.date.issued2019-12
dc.date.submitted2019-08
dc.identifier.issn1099-4300
dc.identifier.urihttps://hdl.handle.net/1721.1/124883
dc.description.abstractWhat is the value of just a few bits to a guesser? We study this problem in a setup where Alice wishes to guess an independent and identically distributed (i.i.d.) random vector and can procure a fixed number of k information bits from Bob, who has observed this vector through a memoryless channel. We are interested in the guessing ratio, which we define as the ratio of Alice’s guessing-moments with and without observing Bob’s bits. For the case of a uniform binary vector observed through a binary symmetric channel, we provide two upper bounds on the guessing ratio by analyzing the performance of the dictator (for general k≥1 ) and majority functions (for k=1 ). We further provide a lower bound via maximum entropy (for general k≥1 ) and a lower bound based on Fourier-analytic/hypercontractivity arguments (for k=1 ). We then extend our maximum entropy argument to give a lower bound on the guessing ratio for a general channel with a binary uniform input that is expressed using the strong data-processing inequality constant of the reverse channel. We compute this bound for the binary erasure channel and conjecture that greedy dictator functions achieve the optimal guessing ratio. ©2019en_US
dc.publisherMDPIen_US
dc.relation.isversionof10.3390/e22010039en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceMDPIen_US
dc.titleGuessing with a bit of helpen_US
dc.typeArticleen_US
dc.identifier.citationWeinberger, Nir, and Ofer Shayevitz, "Guessing with a bit of help." Entropy 22, 1 (Dec. 2019): no. 39 doi 10.3390/e22010039 ©2019 Author(s)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Laboratory for Information and Decision Systemsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Data, Systems, and Societyen_US
dc.relation.journalEntropyen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.date.submission2020-03-05T14:46:13Z
mit.journal.volume22en_US
mit.journal.issue1en_US
mit.licensePUBLISHER_CC
mit.metadata.statusComplete


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