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=Questions and Comments= | =Questions and Comments= | ||
| − | * | + | *[Reviewed by Minwoong Kim] |
| − | * Sudhir starts with a very interesting coin example to give us a strong motivation to easily understand what Maximum Likelihood means. | + | * Sudhir starts with a very interesting coin example to give us a strong motivation to easily understand what Maximum Likelihood means. The author states a very clear mathematical definition and a methodology of computing MLE by the first and second order derivatives. Then, the important properties of MLE are described. Finally, the author shows several examples of MLE for the parameters of the Gaussian distribution and Binomial distribution. In summary, this slecture gives us a very clear definition and examples of MLE, but the most of contents seem to be shown and solved in our lecture. That is only a weak point of this slecture. |
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Back to '''[[Mle_tutorial|MLE Tutrial]]''' | Back to '''[[Mle_tutorial|MLE Tutrial]]''' | ||
Latest revision as of 19:35, 28 April 2014
Questions and Comments for: MLE Tutorial
A slecture by Sudhir Kylasa
Please leave me comment below if you have any questions, if you notice any errors or if you would like to discuss a topic further.
Questions and Comments
- [Reviewed by Minwoong Kim]
- Sudhir starts with a very interesting coin example to give us a strong motivation to easily understand what Maximum Likelihood means. The author states a very clear mathematical definition and a methodology of computing MLE by the first and second order derivatives. Then, the important properties of MLE are described. Finally, the author shows several examples of MLE for the parameters of the Gaussian distribution and Binomial distribution. In summary, this slecture gives us a very clear definition and examples of MLE, but the most of contents seem to be shown and solved in our lecture. That is only a weak point of this slecture.
Back to MLE Tutrial
