Month: July 2020

Universal Inference: Review Part I

There’s an important new paper out by Larry Wasserman et al. that describes a very general technique, called Universal Inference, for constructing statistical hypothesis tests and confidence intervals. In the traditional theory of statistics, such as would be taught in an undergraduate mathematical statistics course, a standard way hypothesis tests are constructed and analyzed is… Read More Universal Inference: Review Part I

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Recommendation Systems: From Co-occurrence Counts to Probabilities

In the previous post, we demonstrated how to efficiently compute co-occurrences with matrix algebra and use those calculations to recommend books to users. Though we saw some sensible recommendations come out of this approach, it also suffers from a number of issues, including: The Gatsby Problem: popular books tend to be over represented in the… Read More Recommendation Systems: From Co-occurrence Counts to Probabilities

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