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This slecture is under review by Lu Zhang. | This slecture is under review by Lu Zhang. | ||

+ | Summay: Your slecture is excellent. It definitely gives a good and fairly complete review of Bayes' rule. It is also very well organized, first the definition (What is Bayes' theroem), second the derivation (how to prove the correctness), then the detailed example(Why useful) and finally an introduction of Bayesian classifier (more applications). | ||

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+ | LZ Comment1: I like your example for Bayes' rule. It is a simple, typical and real-world problem of solving the posterior probability. It might also be interesting if you can give an example for the Bayesian classification in the last section, so people who have not seen this before would have an idea how a maximum posterior probability could be used for classification. People will always like it to be used for real-world situations. | ||

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+ | LZ Comment2: Your derivation of Bayes' rule is already very clear. However, it might be better for students to understand and memorize it if you could explain it with some graphs(like Venn diagrams) and list the Axioms of probability required for this derivation. | ||

## Latest revision as of 19:55, 3 May 2014

Questions and Comments for:
**Derivation_of_Bayes_rule_In_Chinese**

A slecture by Weibao Wang

如果你有什么问题或建议，请在下面留言。

# 问题和建议

This slecture is under review by Lu Zhang.

Summay: Your slecture is excellent. It definitely gives a good and fairly complete review of Bayes' rule. It is also very well organized, first the definition (What is Bayes' theroem), second the derivation (how to prove the correctness), then the detailed example(Why useful) and finally an introduction of Bayesian classifier (more applications).

LZ Comment1: I like your example for Bayes' rule. It is a simple, typical and real-world problem of solving the posterior probability. It might also be interesting if you can give an example for the Bayesian classification in the last section, so people who have not seen this before would have an idea how a maximum posterior probability could be used for classification. People will always like it to be used for real-world situations.

LZ Comment2: Your derivation of Bayes' rule is already very clear. However, it might be better for students to understand and memorize it if you could explain it with some graphs(like Venn diagrams) and list the Axioms of probability required for this derivation.