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micromath.wordpress.com | ||
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www.jeremykun.com
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| | | | | Last time we investigated the naive (which I'll henceforth call "classical") notion of the Fourier transform and its inverse. While the development wasn't quite rigorous, we nevertheless discovered elegant formulas and interesting properties that proved useful in at least solving differential equations. Of course, we wouldn't be following this trail of mathematics if it didn't result in some worthwhile applications to programming. While we'll get there eventually, this primer will take us deeper down the rabbit hole of abstraction. | |
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rjlipton.com
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| | | | | Ideas from algebraic geometry and arithmetic complexity Hyman Bass is a professor of both mathematics and mathematics education at the University of Michigan, after a long and storied career at Columbia University. He was one of the first generation of mathematicians to investigate K-theory, and gave what is now the recognized definition of the first... | |
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joe-kirby.com
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| | | | | Cognitive science shows us what makes instruction effective. Long ago, primitive peoples attempted to fly by strapping feathered wings to their arms andleaping off cliffs from great heights, flapping with all their might. Despite their dreams and hard work, they invariably failed. Flight only became possible once people understood the laws of gravity, forces and... | |
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nelari.us
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| | | In inverse transform sampling, the inverse cumulative distribution function is used to generate random numbers in a given distribution. But why does this work? And how can you use it to generate random numbers in a given distribution by drawing random numbers from any arbitrary distribution? | ||
