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7.1 due November 16

The part I struggled with the most in this section was figuring out what a monte carlo method was. It seemed to give a general introduction/description and then an example, but never the actual definition. I figured it out, but it took me a bit to realize I needed to.

I like the estimates of pi examples. I think my kids would, too.

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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.