| (3 intermediate revisions by 2 users not shown) | |||
| Line 4: | Line 4: | ||
[[Category:ECE662]] | [[Category:ECE662]] | ||
[[Category:pattern recognition]] | [[Category:pattern recognition]] | ||
| − | |||
<center><font size= 4> | <center><font size= 4> | ||
| Line 18: | Line 17: | ||
==Review== | ==Review== | ||
| − | + | This is reviewed by Joonsoo Kim. | |
| + | |||
| + | It is good lecture to understand What MLE is. It explain how to get a solution of MLE well and what the pros and cons for are for using MLE. It shows several examples to solve MLE for simple distributions. Last, it shows what numerical optimizations of MLE are widely used. | ||
| + | |||
---- | ---- | ||
---- | ---- | ||
==Comments/Feedback== | ==Comments/Feedback== | ||
| − | + | When it explain about the pros and cons of MLE, it might be better to give an example of another famous estimator and compare MLE and that estimator. Then, we can more easily understand the properties of MLE. And I wish that the numerical optimization of MLE should have been explained in detail. | |
---- | ---- | ||
[[ SlectureKeehwanECE662Spring14|Back to SlectureKeehwanECE662Spring14]] | [[ SlectureKeehwanECE662Spring14|Back to SlectureKeehwanECE662Spring14]] | ||
Latest revision as of 10:36, 2 May 2014
Review on Maximum Likelihood Estimation (MLE): its properties and examples
A slecture by graduate student Keehwan Park
Loosely based on the ECE662 Spring 2014 lecture material of Prof. Mireille Boutin.
Review
This is reviewed by Joonsoo Kim.
It is good lecture to understand What MLE is. It explain how to get a solution of MLE well and what the pros and cons for are for using MLE. It shows several examples to solve MLE for simple distributions. Last, it shows what numerical optimizations of MLE are widely used.
Comments/Feedback
When it explain about the pros and cons of MLE, it might be better to give an example of another famous estimator and compare MLE and that estimator. Then, we can more easily understand the properties of MLE. And I wish that the numerical optimization of MLE should have been explained in detail.
