Date of Award
Spring 5-8-2019
Degree Name
Master of Science (MS)
Degree Type
Thesis
Abstract
Classical methods for psychometric function estimation either require excessive resources to perform, as in the method of constants, or produce only a low resolution approximation of the target psychometric function, as in adaptive staircase or up-down procedures. This thesis makes two primary contributions to the estimation of the audiogram, a clinically relevant psychometric function estimated by querying a patient’s for audibility of a collection of tones. First, it covers the implementation of a Gaussian process model for learning an audiogram using another audiogram as a prior belief to speed up the learning procedure. Second, it implements a use case of Bayesian active model selection to determine whether two audiograms differ. Both algorithms were tested using audiometric data from the National Institute for Occupational Safety and Health (NIOSH).
Language
English (en)
Chair
Dennis Barbour
Committee Members
Roman Garnett Marion Neumann
Comments
Permanent URL: https://doi.org/7936/9qzj-4x62