Average of function vs. function evaluated at average

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The discussion centers on the conditions under which the average of a function f(x) exceeds the function evaluated at the average value m of a random variable x. It highlights the relevance of Jensen's inequality, indicating that if f is a concave function, the average of f(x) will always be greater than f(m), regardless of the probability distribution p(x). Participants emphasize that the concavity of the function is crucial to this relationship. The conversation suggests that understanding the properties of concave and convex functions is essential for determining these conditions. The inquiry seeks further clarification on theorems related to this mathematical concept.
nigeisel
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Dear everyone,
consider a function f(x) of a random variable x with average m and probability distribution p(x).

I would like to know under which conditions the average of f(x) is greater than f(m), i.e., under which conditions is the average of the function greater than the function evaluated at the average value of the random variable.

\int p(x) f(x) dx > f(m) ?

Does anyone know about theorems that might help me? I suppose it depends on the concaveness of the function f and shape of the distribution p.

I would be very grateful for help.
Nico
 
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If the function is concave, it does not depend on tha shape of p. See http://en.wikipedia.org/wiki/Jensen%27s_inequality"
 
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In fact, that property is often used as the very definition of concave, convex, and linear.
 
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