Normal Bayesian two-armed bandits Fahrenholtz, Steven
dc.contributor.department Statistics 2018-08-15T15:45:46.000 2020-07-02T06:06:17Z 2020-07-02T06:06:17Z Fri Jan 01 00:00:00 UTC 1982 1982
dc.description.abstract <p>The undiscounted normal two-armed bandit is examined from a Bayesian point of view for independent and singular priors on the mean vector ((theta)(,1),(theta)(,2)). Quantification is given to the well-accepted notion that an apparently inferior source needs to be sampled now and then. The optimal strategy is defined in terms of the source differential function, (DELTA)('n) = V(,y)('n) - V(,x)('n), where V(,x)('n) and V(,y)('n) are the valuations of sampling the two respective sources. For the independent prior case, bounds and linear approximations for (DELTA)('n) are obtained by recursion. The limiting behavior of (DELTA)('n) is discussed, in terms of certain summary parameters of location and information. In the more tractable singular case, the optimal strategy is myopic in the case of equal prior information on both sources.</p>
dc.format.mimetype application/pdf
dc.identifier archive/
dc.identifier.articleid 9342
dc.identifier.contextkey 6333691
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath rtd/8343
dc.language.iso en
dc.source.bitstream archive/|||Sat Jan 15 02:10:10 UTC 2022
dc.subject.disciplines Statistics and Probability
dc.subject.keywords Statistics
dc.title Normal Bayesian two-armed bandits
dc.type article
dc.type.genre dissertation
dspace.entity.type Publication
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca dissertation Doctor of Philosophy
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