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=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. The author starts of by giving a general introduction to Bayesian Parametric Distribution, and goes on to derive the "posteriori" parameters of the MEAN of a univariate Gaussian Distribution |
| + | [Review by Hariharan Seshadri]: | ||
| + | * Having obtained these "posteriori" 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 compare the accuracy of BPE and MLE in a classification context (just like HW 2 - Spring 2014). In short, it was a very informative slecture | ||
| + | |||
---- | ---- | ||
[[2014_Spring_ECE_662_Boutin|Back to ECE 662 S14 course wiki]] | [[2014_Spring_ECE_662_Boutin|Back to ECE 662 S14 course wiki]] | ||
[[ECE662|Back to ECE 662 course page]] | [[ECE662|Back to ECE 662 course page]] | ||
Revision as of 19:40, 7 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. The author starts of by giving a general introduction to Bayesian Parametric Distribution, and goes on to derive the "posteriori" parameters of the MEAN of a univariate Gaussian Distribution
[Review by Hariharan Seshadri]:
- Having obtained these "posteriori" 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 compare the accuracy of BPE and MLE in a classification context (just like HW 2 - Spring 2014). In short, it was a very informative slecture
