Personalized Dynamic Pricing Based on Improved Thompson Sampling
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- Nicholas G. Polson & James G. Scott & Jesse Windle, 2013. "Bayesian Inference for Logistic Models Using Pólya--Gamma Latent Variables," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(504), pages 1339-1349, December.
- Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
- Patrick Bajari & Denis Nekipelov & Stephen P. Ryan & Miaoyu Yang, 2015. "Machine Learning Methods for Demand Estimation," American Economic Review, American Economic Association, vol. 105(5), pages 481-485, May.
- Andrew Rhodes & Jidong Zhou, 2022.
"Personalized Pricing and Competition,"
Cowles Foundation Discussion Papers
2329, Cowles Foundation for Research in Economics, Yale University.
- Rhodes, Andrew & Zhou, Jidong, 2022. "Personalized Pricing and Competition," MPRA Paper 112988, University Library of Munich, Germany.
- Rhodes, Andrew & Zhou, Jidong, 2022. "Personalized Pricing and Competition," TSE Working Papers 22-1333, Toulouse School of Economics (TSE), revised Mar 2024.
- Peter Seele & Claus Dierksmeier & Reto Hofstetter & Mario D. Schultz, 2021. "Mapping the Ethicality of Algorithmic Pricing: A Review of Dynamic and Personalized Pricing," Journal of Business Ethics, Springer, vol. 170(4), pages 697-719, May.
- Cao, Ping & Zhao, Nenggui & Wu, Jie, 2019. "Dynamic pricing with Bayesian demand learning and reference price effect," European Journal of Operational Research, Elsevier, vol. 279(2), pages 540-556.
- Jean-Pierre Dubé & Sanjog Misra, 2023. "Personalized Pricing and Consumer Welfare," Journal of Political Economy, University of Chicago Press, vol. 131(1), pages 131-189.
- Steinberg, Etye, 2020. "Big Data and Personalized Pricing," Business Ethics Quarterly, Cambridge University Press, vol. 30(1), pages 97-117, January.
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Keywords
personalized dynamic pricing; demand learning; Thompson sampling algorithm; Bayesian inference; Pólya-gamma distribution;All these keywords.
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