# Jensen's inequality

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In mathematics, **Jensen's inequality**, named after the Danish mathematician Johan Jensen, relates the value of a convex function of an integral to the integral of the convex function. It was proved by Jensen in 1906.^{[1]} Given its generality, the inequality appears in many forms depending on the context, some of which are presented below. In its simplest form the inequality states that the convex transformation of a mean is less than or equal to the mean after convex transformation; it is a simple corollary that the opposite is true of concave transformations.

Jensen's inequality generalizes the statement that the secant line of a convex function lies *above* the graph of the function, which is Jensen's inequality for two points: the secant line consists of weighted means of the convex function,

while the graph of the function is the convex function of the weighted means,

In the context of probability theory, it is generally stated in the following form: if *X* is a random variable and Template:Mvar is a convex function, then

## Statements

The classical form of Jensen's inequality involves several numbers and weights. The inequality can be stated quite generally using either the language of measure theory or (equivalently) probability. In the probabilistic setting, the inequality can be further generalized to its *full strength*.

### Finite form

For a real convex function Template:Mvar, numbers *x*_{1}, *x*_{2}, ..., *x _{n}* in its domain, and positive weights

*a*, Jensen's inequality can be stated as:

_{i}and the inequality is reversed if Template:Mvar is concave, which is

Equality holds if and only if or Template:Mvar is linear.

As a particular case, if the weights *a _{i}* are all equal, then (1) and (2) become

For instance, the function log(*x*) is *concave*, so substituting *φ*(*x*) = log(*x*) in the previous formula (4) establishes the (logarithm of) the familiar arithmetic mean-geometric mean inequality:

A common application has *x* as a function of another variable (or set of variables) *t*, that is, *x _{i}* =

*g*(

*t*). All of this carries directly over to the general continuous case: the weights

_{i}*a*are replaced by a non-negative integrable function

_{i}*f*(

*x*), such as a probability distribution, and the summations are replaced by integrals.

### Measure-theoretic and probabilistic form

Let (Ω, *A*, *μ*) be a measure space, such that *μ*(Ω) = 1. If *g* is a real-valued function that is μ-integrable, and if Template:Mvar is a convex function on the real line, then:

In real analysis, we may require an estimate on

where *a*, *b* ∈ **R**, and *f* : [*a*, *b*] → **R** is a non-negative Lebesgue-integrable function. In this case, the Lebesgue measure of [*a*, *b*] need not be unity. However, by integration by substitution, the interval can be rescaled so that it has measure unity. Then Jensen's inequality can be applied to get

The same result can be equivalently stated in a probability theory setting, by a simple change of notation. Let be a probability space, *X* an integrable real-valued random variable and Template:Mvar a convex function. Then:

In this probability setting, the measure Template:Mvar is intended as a probability , the integral with respect to Template:Mvar as an expected value , and the function *g* as a random variable *X*.

Notice that the equality holds if and only if Template:Mvar is constant (degenerate random variable) or Template:Mvar is linear.

### General inequality in a probabilistic setting

More generally, let *T* be a real topological vector space, and *X* a *T*-valued integrable random variable. In this general setting, *integrable* means that there exists an element in *T*, such that for any element *z* in the dual space of *T*: , and . Then, for any measurable convex function Template:Mvar and any sub-σ-algebra of :

Here stands for the expectation conditioned to the σ-algebra . This general statement reduces to the previous ones when the topological vector space Template:Mvar is the real axis, and is the trivial Template:Mvar-algebra {∅, Ω}.

(Attention: In this generality additional assumptions on the convex function and/ or the topological vector space are needed, see Example (1.3) on p. 53 in.^{[2]})

## Proofs

Jensen's inequality can be proved in several ways, and three different proofs corresponding to the different statements above will be offered. Before embarking on these mathematical derivations, however, it is worth analyzing an intuitive graphical argument based on the probabilistic case where Template:Mvar is a real number (see figure). Assuming a hypothetical distribution of Template:Mvar values, one can immediately identify the position of and its image in the graph. Noticing that for convex mappings *Y* = *φ*(*X*) the corresponding distribution of Template:Mvar values is increasingly "stretched out" for increasing values of *X*, it is easy to see that the distribution of Template:Mvar is broader in the interval corresponding to *X* > *X*_{0} and narrower in *X* < *X*_{0} for any *X*_{0}; in particular, this is also true for . Consequently, in this picture the expectation of Template:Mvar will always shift upwards with respect to the position of . A similar reasoning holds if the distribution of Template:Mvar covers a decreasing portion of the convex function, or both a decreasing and an increasing portion of it. This "proves" the inequality, i.e.

with equality when *φ*(*X*) is not strictly convex, e.g. when it is a straight line, or when Template:Mvar follows a degenerate distribution (i.e. is a constant).

The proofs below formalize this intuitive notion.

### Proof 1 (finite form)

If *λ*_{1} and *λ*_{2} are two arbitrary nonnegative real numbers such that *λ*_{1} + *λ*_{2} = 1 then convexity of Template:Mvar implies

This can be easily generalized: if *λ*_{1}, ..., *λ _{n}* are nonnegative real numbers such that

*λ*

_{1}+ ... +

*λ*= 1, then

_{n}for any *x*_{1}, ..., *x _{n}*. This

*finite form*of the Jensen's inequality can be proved by induction: by convexity hypotheses, the statement is true for

*n*= 2. Suppose it is true also for some

*n*, one needs to prove it for

*n*+ 1. At least one of the

*λ*is strictly positive, say

_{i}*λ*

_{1}; therefore by convexity inequality:

Since

one can apply the induction hypotheses to the last term in the previous formula to obtain the result, namely the finite form of the Jensen's inequality.

In order to obtain the general inequality from this finite form, one needs to use a density argument. The finite form can be rewritten as:

where *μ*_{n} is a measure given by an arbitrary convex combination of Dirac deltas:

Since convex functions are continuous, and since convex combinations of Dirac deltas are weakly dense in the set of probability measures (as could be easily verified), the general statement is obtained simply by a limiting procedure.

### Proof 2 (measure-theoretic form)

Let *g* be a real-valued μ-integrable function on a probability space Ω, and let Template:Mvar be a convex function on the real numbers. Since Template:Mvar is convex, at each real number Template:Mvar we have a nonempty set of subderivatives, which may be thought of as lines touching the graph of Template:Mvar at Template:Mvar, but which are at or below the graph of Template:Mvar at all points.

Now, if we define

because of the existence of subderivatives for convex functions, we may choose *a* and *b* such that

for all real x and

But then we have that

for all x. Since we have a probability measure, the integral is monotone with *μ*(Ω) = 1 so that

as desired.

### Proof 3 (general inequality in a probabilistic setting)

Let *X* be an integrable random variable that takes values in a real topological vector space *T*. Since *φ* : *T* → **R** is convex, for any , the quantity

is decreasing as Template:Mvar approaches 0^{+}. In particular, the *subdifferential* of Template:Mvar evaluated at *x* in the direction Template:Mvar is well-defined by

It is easily seen that the subdifferential is linear in Template:Mvar {{ safesubst:#invoke:Unsubst||date=__DATE__ |$B=
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In particular, for an arbitrary sub-Template:Mvar-algebra we can evaluate the last inequality when to obtain

Now, if we take the expectation conditioned to on both sides of the previous expression, we get the result since:

by the linearity of the subdifferential in the *y* variable, and the following well-known property of the conditional expectation:

## Applications and special cases

### Form involving a probability density function

Suppose Ω is a measurable subset of the real line and *f*(*x*) is a non-negative function such that

In probabilistic language, *f* is a probability density function.

Then Jensen's inequality becomes the following statement about convex integrals:

If *g* is any real-valued measurable function and Template:Mvar is convex over the range of *g*, then

If *g*(*x*) = *x*, then this form of the inequality reduces to a commonly used special case:

### Alternative finite form

Let Ω = {*x*_{1}, ... *x _{n}*}, and take Template:Mvar to be the counting measure on Ω, then the general form reduces to a statement about sums:

provided that *λ _{i}* ≥ 0 and

There is also an infinite discrete form.

### Statistical physics

Jensen's inequality is of particular importance in statistical physics when the convex function is an exponential, giving:

where the expected values are with respect to some probability distribution in the random variable Template:Mvar.

The proof in this case is very simple (cf. Chandler, Sec. 5.5). The desired inequality follows directly, by writing

and then applying the inequality *e ^{X}* ≥ 1 +

*X*to the final exponential.

### Information theory

If *p*(*x*) is the true probability distribution for Template:Mvar, and *q*(*x*) is another distribution, then applying Jensen's inequality for the random variable *Y*(*x*) = *q*(*x*)/*p*(*x*) and the function *φ*(*y*) = −log(*y*) gives

Therefore:

a result called Gibbs' inequality.

It shows that the average message length is minimised when codes are assigned on the basis of the true probabilities *p* rather than any other distribution *q*. The quantity that is non-negative is called the Kullback–Leibler divergence of *q* from *p*.

Since −log(*x*) is a strictly convex function for *x* > 0, it follows that equality holds when *p*(*x*) equals *q*(*x*) almost everywhere.

### Rao–Blackwell theorem

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If *L* is a convex function, then from Jensen's inequality we get

So if δ(*X*) is some estimator of an unobserved parameter θ given a vector of observables *X*; and if *T*(*X*) is a sufficient statistic for θ; then an improved estimator, in the sense of having a smaller expected loss *L*, can be obtained by calculating

the expected value of δ with respect to θ, taken over all possible vectors of observations *X* compatible with the same value of *T*(*X*) as that observed.

This result is known as the Rao–Blackwell theorem.

## See also

- Karamata's inequality for a more general inequality
- Popoviciu's inequality
- Law of averages
- Proof without words

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## Notes

## References

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- Tristan Needham (1993) "A Visual Explanation of Jensen's Inequality", American Mathematical Monthly 100(8):768–71.
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## External links

- Jensen's Operator Inequality of Hansen and Pedersen.
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