Degree Type

Dissertation

Date of Award

1982

Degree Name

Doctor of Philosophy

Department

Statistics

Abstract

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.

DOI

https://doi.org/10.31274/rtd-180813-7952

Publisher

Digital Repository @ Iowa State University, http://lib.dr.iastate.edu/

Copyright Owner

Steven K. Fahrenholtz

Language

en

Proquest ID

AAI8307746

File Format

application/pdf

File Size

108 pages

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