| (5 intermediate revisions by 2 users not shown) | |||
| Line 1: | Line 1: | ||
<center><font size= 5> | <center><font size= 5> | ||
| − | [[ | + | Questions and Comments for [[Bayersian_Parameter_Estimation:_Gaussian_Case|Bayesian Parameter Estimation: Gaussian Case]] |
</font size> | </font size> | ||
| − | A [https://www.projectrhea.org/learning/slectures.php slecture] by [[ECE]] student | + | A [https://www.projectrhea.org/learning/slectures.php slecture] by [[ECE]] student Shaobo Fang |
| − | |||
</center> | </center> | ||
---- | ---- | ||
| Line 18: | Line 17: | ||
=Questions and Comments= | =Questions and Comments= | ||
| − | + | [Review by Hariharan Seshadri]: | |
| − | - | + | * This is a very well developed slecture on Bayesian Parametric Estimation (BPE) |
| + | |||
| + | [Review by Hariharan Seshadri]: | ||
| + | |||
| + | * What I like about this slecture is that the author is organized in his/her thoughts. | ||
| + | |||
| + | [Review by Hariharan Seshadri]: | ||
| + | |||
| + | * The author starts of by giving a general introduction to Bayesian Parametric Distribution, and goes on to derive the parameters (mean and std. deviation) of the distribution of the MEAN of a univariate Gaussian Distribution (Assuming that the MEAN is a Normally Distributed Random Variable and also assuming that the some prior information about the MEAN is already known along with the std. deviation of the distribution from which the samples were obtained from) | ||
| + | |||
| + | [Review by Hariharan Seshadri]: | ||
| + | |||
| + | * Having obtained these parameters of the MEAN, the author goes on to derive the conditional probability of p(x|D) using the afore-mentioned parameters | ||
| + | |||
| + | [Review by Hariharan Seshadri]: | ||
| + | |||
| + | * The author is methodical in the derivation | ||
| + | [Review by Hariharan Seshadri]: | ||
| − | * | + | * To make this great slecture even better, the author could in future consider comparing the accuracy of BPE and MLE in a classification context (just like HW 2 - Spring 2014). In short, it was a very informative slecture |
| + | |||
| + | =Author's Reply= | ||
| + | Thanks a lot for your input and the effort reviewing the selecture. | ||
---- | ---- | ||
Latest revision as of 16:04, 12 May 2014
Questions and Comments for Bayesian Parameter Estimation: Gaussian Case
This is the talk page for the slecture notes on . Please leave me a 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
[Review by Hariharan Seshadri]:
- This is a very well developed slecture on Bayesian Parametric Estimation (BPE)
[Review by Hariharan Seshadri]:
- What I like about this slecture is that the author is organized in his/her thoughts.
[Review by Hariharan Seshadri]:
- The author starts of by giving a general introduction to Bayesian Parametric Distribution, and goes on to derive the parameters (mean and std. deviation) of the distribution of the MEAN of a univariate Gaussian Distribution (Assuming that the MEAN is a Normally Distributed Random Variable and also assuming that the some prior information about the MEAN is already known along with the std. deviation of the distribution from which the samples were obtained from)
[Review by Hariharan Seshadri]:
- Having obtained these parameters of the MEAN, the author goes on to derive the conditional probability of p(x|D) using the afore-mentioned parameters
[Review by Hariharan Seshadri]:
- The author is methodical in the derivation
[Review by Hariharan Seshadri]:
- To make this great slecture even better, the author could in future consider comparing the accuracy of BPE and MLE in a classification context (just like HW 2 - Spring 2014). In short, it was a very informative slecture
Author's Reply
Thanks a lot for your input and the effort reviewing the selecture.
