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4.1 due October 8

I understand the theory of this section I think, but I'm not sure how well that translates to being able to use it in practice. I can follow the example code in the book, but I don't think I could write it yet.

The coin problem is a little funny to read because I do it so differently in my head. You just use as many of the biggest coins as you can and then repeat for the next size down until you get to pennies. It works with our money system, but it baffles me it doesn't work for some others. And both ways to find the optimal number of coins, while good, seem overkill. (They aren't, because they would work with any money system. They just seem like they are because all of my experience says they are.)

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7.2 due November 19

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.

7.3 due November 20

This section seemed pretty straight forward. The trickiest part was remembering the big O costs of things. This section seems like it would fit with the tree stuff we did. Maybe the sections we are skipping make sense why it is here in the book.

6.3 due November 9

This section seems pretty straight forward. I don't follow every step of every proof, (I don't have enough statistics understanding to know what we can and can't do.) but generally it makes sense. It's always fun to study the parts of math and science that unexpectedly show order where we don't expect it. I love seeing structure and order built into the world.