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'''Jianxin Sun''' will review this slecture | '''Jianxin Sun''' will review this slecture | ||
| − | In this slecture, the author | + | In this slecture, the author details the method of MLE on different specific distribution and conclude the final expression on how to estimate each of them. |
| − | This slecture starts with the basic idea of Maximum likelihood estimation(MLE) and use Normal Distribution as an example to show how to use MLE on | + | This slecture starts with the basic idea of Maximum likelihood estimation(MLE) and use Normal Distribution as an example to show how to use MLE on a specific distribution. 5 commonly used distributions are investigated, including Exponential, Geometric, Binomial, Possision and uniform distributions. Mathmatical derivation are clearly presented which helps student to understand how to apply general MLE on a new distribution. This slecture also summerizes the final useful expression of estimation for each of those distribtions which is very handy and can be directely used for application. |
I like the orgnization of this slecture for its formal mathmatical expressions and proper stress on the important points. | I like the orgnization of this slecture for its formal mathmatical expressions and proper stress on the important points. | ||
Revision as of 10:25, 5 May 2014
Questions and Comments for Maximum Likelihood Estimation (MLE) for various probability distributions
A slecture by Hariharan Seshadri
Please leave comments below if you have any questions, or if you notice any errors, or if you would like to discuss a topic further.
Jianxin Sun will review this slecture
In this slecture, the author details the method of MLE on different specific distribution and conclude the final expression on how to estimate each of them.
This slecture starts with the basic idea of Maximum likelihood estimation(MLE) and use Normal Distribution as an example to show how to use MLE on a specific distribution. 5 commonly used distributions are investigated, including Exponential, Geometric, Binomial, Possision and uniform distributions. Mathmatical derivation are clearly presented which helps student to understand how to apply general MLE on a new distribution. This slecture also summerizes the final useful expression of estimation for each of those distribtions which is very handy and can be directely used for application.
I like the orgnization of this slecture for its formal mathmatical expressions and proper stress on the important points.
Back to Maximum Likelihood Estimation (MLE) for various probability distributions
