Bayesian Applications in Auditory Research

• Published in: Journal of speech, language, and hearing research Vol. 62; no. 3; pp. 577 - 586
• Main Authors: McMillan, Garnett P, Cannon, John B
• Format: Journal Article
• Language: English
• Published: United States American Speech-Language-Hearing Association 01.03.2019
• Subjects: Age Factors; Aged; Audiology; Audiology - methods; Auditory Perception; Bayes Theorem; Bayesian analysis; Bayesian Statistics; Biomedical Research - methods; Hearing disorders; Hearing therapy; Humans; Inference; Loudness Perception; Markov analysis; Markov Chains; Markov Processes; Methods; Middle Aged; Monte Carlo Method; Monte Carlo Methods; Physiological research; Probability; Research Methodology; Scientists; Sound; Sound intensity; Sound therapy; Statistical Inference; Therapy; Tinnitus; Tinnitus - diagnosis; Tinnitus - therapy; Treatment Outcome
• ISSN: 1092-4388; 1558-9102
• DOI: 10.1044/2018_JSLHR-H-ASTM-18-0228

Purpose: This article presents a basic exploration of Bayesian inference to inform researchers unfamiliar to this type of analysis of the many advantages this readily available approach provides. Method: First, we demonstrate the development of Bayes' theorem, the cornerstone of Bayesian statistics, into an iterative process of updating priors. Working with a few assumptions, including normalcy and conjugacy of prior distribution, we express how one would calculate the posterior distribution using the prior distribution and the likelihood of the parameter. Next, we move to an example in auditory research by considering the effect of sound therapy for reducing the perceived loudness of tinnitus. In this case, as well as most real-world settings, we turn to Markov chain simulations because the assumptions allowing for easy calculations no longer hold. Using Markov chain Monte Carlo methods, we can illustrate several analysis solutions given by a straight forward Bayesian approach. Conclusion: Bayesian methods are widely applicable and can help scientists overcome analysis problems, including how to include existing information, run interim analysis, achieve consensus through measurement, and, most importantly, interpret results correctly.