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Research on the application of Markov chain
- 1 Harrow International School Beijing
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Abstract
In modern times, mathematicians are often troubled by new approaches to deducing the outcomes of certain events. The introduction of Markov models eliminated queries across different sectors. In 1907, Russian mathematician Andrey Markov proposed the concept of Markov chains. It has been widely used in many aspects, such as weather prediction, deep learning, biological information, and so on. Therefore, this paper examines how the Markov chain model can be applied in a variety of situations. This study uses Python as a supporting tool to simulate states and possible outcomes. It can be concluded that Python is able to simulate the state transitions of the Markov chain. The paper also identifies the differences between Markov models, their application in common scenarios such as medical, finance, weather forecasting, machine learning and others in our everyday life and why they are so popularly used, including the simplicity of the model and more.
Keywords
Markov chain, transition probabilities, decision making, predictive modeling.
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Cite this article
Kwok,A. (2023). Research on the application of Markov chain. Theoretical and Natural Science,11,147-150.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2023 International Conference on Mathematical Physics and Computational Simulation
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