I enjoyed reading through this chapter because it crosses back into some of the familiar territory of numerical methods and estimations, such as Simpson's Rule. It's neat because it's easy to see the applications of random algorithms, such as estimating pi or finding the area under a complicated curve.
I still struggle to see the bridge between statistics/probability and this material, however. For example, how does the indicator random variable play in to estimating an integral? Also, how do we know how to set up these estimations? The intuition to follow is a lot easier than it is to reproduce.
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