• The Korean Journal of Applied Statistics
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• v.37 no.5
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• pp.663-673
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• 2024

The multi-armed bandits (MAB) problem, involves selecting actions to maximize rewards within dynamic environments. This study explores the application of Thompson sampling, a robust MAB algorithm, within the context of big data analytics and statistical learning theory. By leveraging large-scale banner click data from recommendation systems, we evaluate Thompson sampling's performance across various simulated scenarios, employing advanced approximation techniques. Our findings demonstrate that Thompson sampling, particularly with Langevin Monte Carlo approximation, maintains robust performance and scalability in big data environments. This underscores its practical significance and adaptability, aligning with contemporary challenges in statistical learning.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1960-1963
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• 2021

In this paper, we propose a multiplay Thompson sampling algorithm in multipath communication system. Multipath communication system has advantages on communication capacity, robustness, survivability, and so on. It is important to select appropriate network path according to the status of individual path. However, it is hard to obtain the information of path quality simultaneously. To solve this issue, we propose Thompson sampling which is popular in machine learning area. We find some issues when the algorithm is applied directly in the proposal system and suggested some modifications. Through simulation, we verified the proposed algorithm can utilize the entire network paths. In summary, our proposed algorithm can be applied as a path allocation in multipath-based communications system.