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To offer the readers a better technical background, the author first explained the general Maximum Likelihood Ratio test given 'c' classes. | To offer the readers a better technical background, the author first explained the general Maximum Likelihood Ratio test given 'c' classes. | ||
| − | After the general theorems were established, the author then attempted to investigate the mean of Gaussian R.V. with known <math>\Sigma</math>. | + | After the general theorems were established, the author then attempted to investigate the mean of Gaussian R.V. with known <math>\Sigma</math>. To present the MLE estimation in more details, the case of maximum likelihood estimation examples for Gaussian R.V. both <math>\mu</math> and <math>\sigma</math> unknown was also investigated and is truely interesting since in real it is likely that the data coming in have no known parameters. |
| + | What makes it better is that at the very end author also present a brief discussion of Biasness of the sestimation. | ||
| − | + | Overall, this a great selecture and a great deal of effort from the author with detailed mathmatical derivation. | |
| + | |||
| + | Although it is already very good overall, it might be a bit better for the readers if more context could be presentd to provide better transition from different parts. | ||
| + | |||
| + | =Minor Typo= | ||
| + | 'Bias: The maximum likelihood for the variance $\sigma^2$ is biased means.', $\sigma^2$ was not transformed to Wiki format. | ||
Latest revision as of 17:01, 12 May 2014
Questions and Comments for: Expected Value of MLE estimate over standard deviation and expected deviation
A slecture by Zhenpeng Zhao
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
Updated Review Shaobo Fang:
Now the format issue is gone and the slecture looks great!
The mathmatical derivation is clear, thorough and in details, which is extremely impressive.
To offer the readers a better technical background, the author first explained the general Maximum Likelihood Ratio test given 'c' classes.
After the general theorems were established, the author then attempted to investigate the mean of Gaussian R.V. with known $ \Sigma $. To present the MLE estimation in more details, the case of maximum likelihood estimation examples for Gaussian R.V. both $ \mu $ and $ \sigma $ unknown was also investigated and is truely interesting since in real it is likely that the data coming in have no known parameters.
What makes it better is that at the very end author also present a brief discussion of Biasness of the sestimation.
Overall, this a great selecture and a great deal of effort from the author with detailed mathmatical derivation.
Although it is already very good overall, it might be a bit better for the readers if more context could be presentd to provide better transition from different parts.
Minor Typo
'Bias: The maximum likelihood for the variance $\sigma^2$ is biased means.', $\sigma^2$ was not transformed to Wiki format.
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