Bayesian methods in global optimization

1991 ◽  
Vol 1 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Bruno Betr�
Keyword(s):  
Global Optimization ◽  
Bayesian Methods

Bayesian Applications in Auditory Research

2019 ◽  
Vol 62 (3) ◽  
pp. 577-586 ◽  
Author(s):  
Garnett P. McMillan ◽  
John B. Cannon
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Bayesian Methods ◽  
Prior Distribution ◽  
Interim Analysis ◽  
Iterative Process ◽  
Bayes Theorem ◽  
Sound Therapy ◽  
The Many ◽  
Auditory Research

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 straightforward 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. Supplemental Material https://doi.org/10.23641/asha.7822592

Bayesian Methods Interpret Data Better

2012 ◽  
Author(s):  
John K. Kruschke
Keyword(s):  
Bayesian Methods

Performance of MAP-Bayesian methods for managing loss of metabolite samples in PET ligand studies

2005 ◽  
Vol 25 (1_suppl) ◽  
pp. S627-S627
Author(s):  
Mary E Spilker ◽  
Gjermund Henriksen ◽  
Till Sprenger ◽  
Michael Valet ◽  
Isabelle Stangier ◽  
...  
Keyword(s):  
Bayesian Methods

Global Optimization

1993 ◽  
Author(s):  
Reiner Horst ◽  
Hoang Tuy
Keyword(s):  
Global Optimization

Probability Models in Global Optimization

Informatica ◽  
2016 ◽  
Vol 27 (2) ◽  
pp. 323-334 ◽  
Author(s):  
James M. Calvin
Keyword(s):  
Global Optimization ◽  
Probability Models
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