The thing I didn't understand about this section is why we need to use a distribution Q instead of just sampling uniformly (which I guess is a distribution...) when we are trying to do rejection sampling. I think it is because it speeds things up as it will have fewer rejections than a rectangle would. This section seems like we are learning more of what we just learned. I understand most of it, but I'm not sure I would recognize when I should use any of the techniques in this section.
The thing I find most difficult in this section is following the proofs. While they make sense, they are not as intuitive as the proofs we've done in the past for me. I found it funny that I was mildly surprised for a second that the Euclidean algorithm came from Euclid. Apparently I thought it had to be from China because in my head it is irrevocably connected to the Chinese Remainder theorem.
The trickiest thing for me in this section is understanding the implications of what we are learning. I can read and understand most of the section but I don't always catch or remember how that applies when I'm programming. It has been fun to see how the math applies in the real world and makes a difference. I like that.
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