Pricing problems with Thompson sampling
In 1933, William R. Thompson proposed an algorithm known as Thompson sampling in order to maximise culmulative payo in a multi-armed bandit (MAB) problem. MAB problems have been fre- quently used to model real-life decision making scenarios. This pa- per explores the extension of Thompson sampl...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10356/77144 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | In 1933, William R. Thompson proposed an algorithm known as
Thompson sampling in order to maximise culmulative payo in a
multi-armed bandit (MAB) problem. MAB problems have been fre-
quently used to model real-life decision making scenarios. This pa-
per explores the extension of Thompson sampling to other problems
beyond the MAB setting. More speci cally, Thompson sampling is
applied to product sales using data from a real dataset in a dynamic
pricing setting as part of the multi-product pricing problem. |
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