This is the talk page for the sLecture notes on Bayes Parameter Estimation (BPE) tutorial. 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

This slecture will be reviewed by Weibao Wang.

And here the review goes:

This slecture discussed the concept of Bayes parameter estimation (BPE).

• It gives the definition of BPE.
• It talks about using BPE to solve daily problem and get the conclusion that the Bayes estimator is based on cumulative information or knowledge of unknown parameters, from past and present. The example given in this lecture is illustrative, which could help reader better understand this concept.
• The author gives an example of continuous case using Gaussian random variable.
• The author talks about the effect of sample size on the posterior by fixing the prior.
• The author talks about the effect of prior on the posterior by fixing the sample size.

Overall speaking, this slecture is well written and interesting. There are many plots which clearly illustrates the result. But there are still minor thing that could be improved, like the presentation. For example, the tpyeface of "Figure 8" is different from the others, and even though, we could understand which one is subplot (a) for Figure 8, but it still good to have subtitle (a) and (b) below each of the subplot.

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