Pricing Problems under the Markov Chain Choice Model

2018 ◽  
Vol 28 (1) ◽  
pp. 157-175 ◽  
Author(s):  
James Dong ◽  
A. Serdar Simsek ◽  
Huseyin Topaloglu
Keyword(s):  
Markov Chain ◽  
Choice Model ◽  
Pricing Problems

Constrained Assortment Optimization Under the Markov Chain–based Choice Model

2020 ◽  
Vol 66 (2) ◽  
pp. 698-721 ◽  
Author(s):  
Antoine Désir ◽  
Vineet Goyal ◽  
Danny Segev ◽  
Chun Ye
Keyword(s):  
Markov Chain ◽  
Choice Model ◽  
Assortment Optimization

Revenue Management Under the Markov Chain Choice Model

2017 ◽  
Vol 65 (5) ◽  
pp. 1322-1342 ◽  
Author(s):  
Jacob B. Feldman ◽  
Huseyin Topaloglu
Keyword(s):  
Markov Chain ◽  
Revenue Management ◽  
Choice Model

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

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