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MIA Talks

Primer: Hypothesis testing and measures of dependence

October 25, 2017
Dept. of Computer Science, Harvard University; Harvard/MIT MD-PhD Program, Harvard Medical School

Searching for departures from statistical independence in data is a fundamental problem that has been formalized in a variety of ways. We will cover two frameworks in which this problem has historically been understood. The first is statistical and involves framing the search as a hypothesis test in a finite-sample setting. The second is probabilistic and involves defining functions of random variables that have useful properties in the large-sample limit. We will close with a discussion of common themes underlying measures of dependence arising from each of these paradigms.