Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 11, 17 November 2023
Open
Access | Article
Research on the application of Markov chain
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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.
References
1. Huang, L. H. (2018). Markov chain Models and Data Science Applications. University of California, Merced.
2. Rupinder Sekhon & Roberta Bloom, , De Anza College
3. Fienberg, S. E. (2007). The analysis of cross-classified categorical data. Springer Science & Business Media.
4. Meyn, S. P., & Tweedie, R. L. (2012). Markov chains and stochastic stability. Springer Science & Business Media..
5. K. J. Arrow, "The Use of Control Models in the Analysis of Economic Systems," Proceedings of the International Symposium on the Theory of Time Series, Wiley, 1963.
6. Box, G. E., & Tiao, G. C. (2011). Bayesian inference in statistical analysis. John Wiley & Sons.
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
- ISBN (Print)
- 978-1-83558-133-9
- ISBN (Online)
- 978-1-83558-134-6
- Published Date
- 17 November 2023
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/11/20230395
- Copyright
- 17 November 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited