TheoremBase

Jensen's Inequality

For a probability measure, an integrable function with values in an open interval has its integral in that interval, and a convex function of the integral is at most the integral of the convex function, when the latter is integrable.

Statement

In the setting of Measurable Functions and the Lebesgue Integral: Standing Notation, let (X,F,μ)(X,\mathcal{F},\mu) be a measure space with μ\mu a probability measure. Let II be an interval that is an open subset of (R,dR)(\mathbb{R},d_{\mathbb{R}}) (Metric Spaces: Balls, the Metric Topology, and the Real and Complex Numbers as Metric Spaces (iv)). Let f∈L1(μ)f\in\mathcal{L}^{1}(\mu) with f(x)∈If(x)\in I for every x∈Xx\in X, let φ:I→R\varphi:I\to\mathbb{R} be convex on II, and suppose that the function φ∘f:X→R\varphi\circ f:X\to\mathbb{R}, x↦φ(f(x))x\mapsto\varphi(f(x)), lies in L1(μ)\mathcal{L}^{1}(\mu).

The mean lies in II. ∫Xf dμ∈I\int_{X}f\,d\mu\in I.

Jensen's inequality.

φ(∫Xf dμ)≤∫Xφ∘f dμ.\varphi\Bigl(\int_{X}f\,d\mu\Bigr)\le\int_{X}\varphi\circ f\,d\mu .

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