Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 39, 21 June 2024
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Application of maximum likelihood estimation in various mathematical models
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Abstract
Maximum likelihood estimation is a breakthrough in the history of statistics, which overcomes the main weakness of Bayesian estimation and has been widely used in various fields, such as language and image processing, and system identification, etc. This paper analyzes the application of maximum likelihood estimation on different mathematical models. It is proved that the universality of maximum likelihood estimation plays an important role in promoting the continued in-depth research on maximum likelihood estimation. This paper also analyzes and summarizes the application of maximum likelihood estimation to specific parameters in different mathematical models. In addition, this work conducts research on the application conditions of maximum likelihood estimation and its main properties, such as variability, consistency, and asymptotic normality, etc. In different mathematical models, such as the annealing furnace efficiency system, gamma environmental factors and adaptive algorithm, etc. Therefore, this paper finds the reason for why the maximum likelihood estimate is widely used in various fields.
Keywords
Maximum likelihood estimation, the annealing furnace efficiency system, gamma environmental factors, adaptive echo cancellation algorithm
References
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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 2nd International Conference on Mathematical Physics and Computational Simulation
- ISBN (Print)
- 978-1-83558-463-7
- ISBN (Online)
- 978-1-83558-464-4
- Published Date
- 21 June 2024
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/39/20240594
- Copyright
- 21 June 2024
- 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