Differential Estimation of Audiograms using Gaussian Process Active Model Selection
Trevor J. Larsen · Open Scholarship Institutional Repository (Washington University in St. Louis) · 2019
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).