Differential Estimation Of Audiograms Using Gaussian Process Active Model Selection

作     者：Trevor Larsen 

作者单位：Washington University in St. Louis 

学位级别：硕士

导师姓名：Professor Dennis Barbour

授予年度：2019年

主      题：Audiogram Machine Learning Gaussian Process Model Selection Bayesian Artificial Intelligence and Robotics Diagnosis Engineering 

摘      要：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).