Jensen's Inequality is a fundamental result in convex analysis that states that for a convex function, the value of the function at the expected value of a random variable is less than or equal to the expected value of the function evaluated at that random variable. This concept has significant implications in various fields, including economics, statistics, and potential theory, particularly when analyzing harmonic functions and majorization principles.
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