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| − | A [https://www.projectrhea.org/learning/slectures.php slecture] by [https://engineering.purdue.edu/~ | + | A [https://www.projectrhea.org/learning/slectures.php slecture] by [https://engineering.purdue.edu/~wang1317/ Yu Wang] |
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| + | [Reviewed by Qi Wang] | ||
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| + | In this slecture, the mathematical principle of Bayesian Estimation has been deduced very smoothly and clearly. Then, some numerical experiments were carried on to study the performance of Bayesian Estimation by comparing it with Maximum Likelihood Estimation and Maximum A Posteriori probability (MAP) estimation. | ||
| + | In general, this is a well organized and neatly written slecture. I like this slecture because the author explain things very clearly and makes those concepts easy to understand. Here are some suggestions to make it even better. | ||
| + | * An separate conclusion part could be added at the end of this slecture to sum up what have been done, this will impress readers more. | ||
| + | * Beta distribution is a probability distribution with two parameters. So it is better to specify these two parameters instead of just state the mean of the Beta distribution. | ||
| + | * The performance of BPE in continuous distribution case such as Gaussian distribution may also be studied. | ||
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| − | Back to '''[[ | + | Back to '''[[Bayes_Parameter_Estimation_with_examples|Bayes_Parameter_Estimation_with_examples]]'' |
Latest revision as of 14:47, 12 May 2014
Questions and Comments for: Bayes_Parameter_Estimation_with_examples
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 Qi Wang]
In this slecture, the mathematical principle of Bayesian Estimation has been deduced very smoothly and clearly. Then, some numerical experiments were carried on to study the performance of Bayesian Estimation by comparing it with Maximum Likelihood Estimation and Maximum A Posteriori probability (MAP) estimation.
In general, this is a well organized and neatly written slecture. I like this slecture because the author explain things very clearly and makes those concepts easy to understand. Here are some suggestions to make it even better.
- An separate conclusion part could be added at the end of this slecture to sum up what have been done, this will impress readers more.
- Beta distribution is a probability distribution with two parameters. So it is better to specify these two parameters instead of just state the mean of the Beta distribution.
- The performance of BPE in continuous distribution case such as Gaussian distribution may also be studied.
