Application of Expectation Maximization Algorithm in Estimating Parameter Values of Maximum Likelihood Model
Keywords:Parameter Estimation, Maximum Likelihood, EM Algorithm
Parameter estimation is an estimation of the population parameter values based on data or samples of population. Parameter estimation can be solverd by several methods, one of which is the Maximum Likelihood method. The focus of this research is to estimate the parameter value of a normal distribution data with Maximum Likelihood based on iteration algorithm. The iteration algorithm that will be used is the Expectation Maximation Algorithm with help of Matlab 2016a program. Based on the results obtained that the estimation value of the parameter and for an accident data in Indonesia based on age group with using Expectation Maximization algorithm is and with 2 iterations.
How to Cite
Copyright (c) 2021 Journal of Research in Mathematics Trends and Technology
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to Journal of Research in Mathematics Trends and Technology (JoRMTT) and Faculty of Mathematics and Natural Sciences as well as TALENTA Publisher Universitas Sumatera Utara as publisher of the journal.
Authors still retain the rights to use and share the published articles without written permission from JoRMTT, as long as they follow the Creative Commons Licensing Terms as set forth by Creative Commons. Authors responsible to obtain the license or related copyright issues in their works. JoRMTT shall be released of any liabilities should any problems arise due to authors errors in this matter.
Authors permit JoRMTT to publish and provide the manuscripts in all forms and media for the purpose of publication and dissemination.
JoRMTT will follow COPE’s Code of Conduct and Best Practice Guidelines for Journal Editors to protect the research results and takes allegations of any infringements, plagiarisms, ethical issues, and frauds should those issues arise. The manuscript is attributed as authors' work, and are properly identified.
Works in the JoRMTT are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Users are free to:
- Share (copy and redistribute the material in any medium or format)
- Adapt (remix, transform, and build upon the material)
under the following terms:
- Attribution (must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use)
- NonCommercial (may not use the material for commercial purposes)
- ShareAlike (If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original)
- No additional restrictions (You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits)
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.