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* Has anyone reserved this slecture for a review? If not, then I would like to review it -[[User:Mhossain|Maliha Hossain]] | * Has anyone reserved this slecture for a review? If not, then I would like to review it -[[User:Mhossain|Maliha Hossain]] | ||
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| − | * Reviewed by Yanzhe Cui: This slecture is about the concept of Bayes rule. The author introduced the derivation of Bayes' rule in discrete and continuous cases. Then the author gave a student attend seminar example and tried to use this example to show the advantage of using Bayes rule. The comments are: (1) please add some spaces between equation and equation number. They are confused when I first look at it; (2) what is the '''black box''' in decision tree figure (the left-most one)? if it's hard to show the conditional probability using tree format, you could choose other ways, such as just text; (3) what do you want to show in Venn diagram? Please add labels in two circles using A and B; (4) the author claim that '''<br> We can see now that <math> \textbf{ P}(A|B) > \textbf{P}(A) </math>''', what's your point? | + | * Reviewed by Yanzhe Cui: This slecture is about the concept of Bayes rule. The author introduced the derivation of Bayes' rule in discrete and continuous cases. Then the author gave a student attend seminar example and tried to use this example to show the advantage of using Bayes rule. The comments are: (1) please add some spaces between equation and equation number. They are confused when I first look at it; (2) what is the '''black box''' in decision tree figure (the left-most one)? if it's hard to show the conditional probability using tree format, you could choose other ways, such as just text; (3) what do you want to show in Venn diagram? Please add labels in two circles using A and B; (4) the author claim that '''<br> We can see now that <math> \textbf{ P}(A|B) > \textbf{P}(A) </math>''', but what's your point? You meant that, in any case, <math> \textbf{ P}(A|B) > \textbf{P}(A) </math> are satisfied? It would be better to explain it thoroughly; (5) if the author could provide a continuous example as well, in my opinion, it would be better and complete. Overall, the author did some research about Bayes rule and tried best to make Bayes rule easy to follow. |
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* Additional Questions / Comments | * Additional Questions / Comments | ||
Revision as of 05:01, 7 May 2014
Questions and Comments for: Derivation of Bayes Rule
A slecture by Anonymous7
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
- Has anyone reserved this slecture for a review? If not, then I would like to review it -Maliha Hossain
- Reviewed by Yanzhe Cui: This slecture is about the concept of Bayes rule. The author introduced the derivation of Bayes' rule in discrete and continuous cases. Then the author gave a student attend seminar example and tried to use this example to show the advantage of using Bayes rule. The comments are: (1) please add some spaces between equation and equation number. They are confused when I first look at it; (2) what is the black box in decision tree figure (the left-most one)? if it's hard to show the conditional probability using tree format, you could choose other ways, such as just text; (3) what do you want to show in Venn diagram? Please add labels in two circles using A and B; (4) the author claim that
We can see now that $ \textbf{ P}(A|B) > \textbf{P}(A) $, but what's your point? You meant that, in any case, $ \textbf{ P}(A|B) > \textbf{P}(A) $ are satisfied? It would be better to explain it thoroughly; (5) if the author could provide a continuous example as well, in my opinion, it would be better and complete. Overall, the author did some research about Bayes rule and tried best to make Bayes rule easy to follow. - Additional Questions / Comments
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