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Jensen average

Physicists speak of maximizing entropy, which, if we change the polarity, is like minimizing the various Jensen inequalities. As we minimize a Jensen inequality, the small values tend to get larger while the large values tend to get smaller. For each population of values there is an average value, i.e., a value that tends to get neither larger nor smaller. The average depends not only on the population, but also on the definition of entropy.

Commonly, the pj are positive and $\sum w_j p_j$ is an energy. Typically the total energy, which will be fixed, can be included as a constraint, or we can find some other function to minimize. For example, divide both terms in (3) by the second term and get an expression which is scale invariant; i.e., scaling p leaves (15) unchanged:  
 \begin{displaymath}
{\sum_{j=1}^N w_j f(p_j)\over f\left( 
 \sum_{j=1}^N w_j p_j\right) } \quad \geq \quad 1\end{displaymath} (15)
Because the expression exceeds unity, we are tempted to take a logarithm and make a new function for minimization:  
 \begin{displaymath}
J \eq \ln \left( \sum_j w_j f(p_j)\right)\ -\ 
 \ln \left[ f \left( \sum_j w_j p_j\right)
 \right] \quad \geq \quad 0\end{displaymath} (16)

Given a population pj of positive variants, and an inequality like (16), I am now prepared to define the ``Jensen average'' $\overline{p}$.Suppose there is one element, say pJ, of the population pj that can be given a first-order perturbation, and only a second-order perturbation in J will result. Such an element is in equilibrium and is the Jensen average $\overline{p}$: 
 \begin{displaymath}
0 \eq \left.
{\partial J\over\partial p_J}
 \right] _{p_J = \overline{p}}\end{displaymath} (17)
Let fp denote the derivative of f with respect to its argument. Inserting (16) into (17) gives
\begin{displaymath}
0 \eq {\partial J\over\partial p_J} \eq 
 {w_J\ f_p(p_J) \ov...
 ...\sum_{j=1}^N w_j p_j) w_J\over 
 f\left( \sum w_j p_j \right) }\end{displaymath} (18)
Solving,
\begin{displaymath}
\overline{p} \eq p_J \eq f_p^{-1} \left( e^J
 f_p(\sum_{j=1}^N w_j p_j) \right) \end{displaymath} (19)

But where do we get the function f, and what do we say about the equilibrium value? Maybe we can somehow derive f from the population. If we cannot work out a general theory, perhaps we can at least find the constant $\gamma$,assuming the functional form to be $f=p^\gamma$.


next up previous print clean
Next: Additivity of envelope entropy Up: RELATED CONCEPTS Previous: Prior and posterior distributions
Stanford Exploration Project
10/21/1998