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For stable positive detector pixel means $N_i$ and uniform detector conversion gain $g$, high-count independent Poisson distribution noise gives $\mathrm{Var}(A_i/B_i)\simeq 2/(g{N}_i)$. Thus substituting a global mean in the equal-signal formula yields $g_{\mathrm{naive}}=g/[⟨N_i⟩⟨1/N_i⟩]\le g$, by the Jensen inequality. Equality holds for uniform means. The flat-field correction pattern cancels from the ratio mean but not from the signal-dependent noise. Local signal bins, or a variance fit after normalization of differences by the square root of their local mean, avoid this leading bias.

For a measurable event $A$, applying Jensen inequality to $u\log u$ separately on $A$ and its complement shows that the Kullback-Leibler divergence between two probability measures is at least the divergence between the two event indicators. This reduces a general probability measure comparison to a binary one and is the key step in a proof of Pinsker's inequality.

The logarithm is strictly concave on positive real numbers, as its second derivative is negative. Thus the Jensen inequality gives $\int \log fd\mu \le \log \int fd\mu$ for a probability measure and an integrable positive $f$, whenever the left side is defined. It also controls the negative logarithmic part of convex combinations of strictly positive functions.

For a measurable function $T$, every f-divergence contracts under pushforward:This follows from conditional Jensen inequality applied to the jointly convex perspective $(a,b)\mapsto bf(a/b)$.

Pairwise disjoint subcubes of $\{0,1{\}}^m$ fixing $d_1,\dots ,d_n$ coordinates satisfy ${\sum }_{x=1}^n{2}^{-d_x}\le 1$. By Jensen inequality, their average codimension is at least ${\log }_{2}n$. Consequently a separating family of disjoint set pairs of total incidence at most $\lambda mn$, with $\lambda >0$, has $m\ge \lceil {\log }_{2}n/\lambda \rceil$.

For $Q(\theta \mid {\theta }_0)={\mathbb{E}}_{{\theta }_0}[\log p_{\theta }(Y,Z)\mid Y]$, the Jensen inequality givesThus any expectation-maximization algorithm M-step that increases $Q$ cannot decrease the observed-data likelihood function. Monotonicity does not by itself assert convergence to a global maximum.

Let $\Omega$ be a bounded connected Lipschitz domain, $\alpha >0$, $g\ge 0$ bounded, and $T:L^1\to L^1$ bounded and positivity preserving with $T1\ne 0$. Minimize $\alpha \mathrm{TV}(u)+D_g(u)$ over nonnegative BV space functions. The logarithmic Jensen inequality bounds $\Vert Tu{\Vert }_1$ and variation on sublevels; mean control for positive imaging operators then bounds the full $BV$ norm. Bounded-variation compactness gives strong $L^1$ convergence, including nonnegativity. The scalar fidelity has bounded derivative on $s\ge 0$, so continuity of $T$ makes its integral continuous in that limit. Lower semicontinuity of variation proves existence by the direct method in the calculus of variations. If $g>0$ almost everywhere and $T$ is injective, strict convexity of the fidelity gives uniqueness. For $0<g<1$, zero is the unique minimizer even without injectivity, since zero energy forces both $Tu=0$ and a constant nonnegative $u$.

For $N$ variables with the standard normal distribution, the expected maximum of their absolute values is at most $\sqrt{2\log (2N)}$, even when they are dependent. Bound the exponential of the maximum by the sum of the $2N$ signed exponentials, use the normal moment-generating function, then apply Jensen inequality and minimize over the exponential parameter. The resulting logarithmic growth is useful in simultaneous estimation.

For independent and identically distributed random variables $X,Y$ with a centered multivariate normal distribution, a continuously differentiable function $f$ with bounded gradient, and a convex function $\Psi$ for which the expressions are integrable, the displayed inequality holds. First apply Jensen inequality conditionally to $f(X)-f(Y)$. For the Gaussian rotation of independent copies, the chain rule gives $f(X)-f(Y)={\int }_0^{\pi /2}⟨\nabla f(U_{\theta }),V_{\theta }⟩d\theta$. A second application of Jensen inequality to the uniform measure on $[0,\pi /2]$, followed by the Gaussian rotation of independent copies, proves the inequality. The useful choice $\Psi (v)=e^{\lambda |v|}$ yields a Gaussian concentration inequality through an exponential moment of an absolute standard normal variable.

If real $f$ and $f^2$ belong to the generator domain of a Feller semigroup, Jensen inequality gives $P_t(f^2)-(P_{t}f{)}^2\ge 0$. This expression is zero at $t=0$, so its right derivative is nonnegative, proving the inequality. It is equivalent to positivity of the carré du champ.

For an exact dimensionless Hamiltonian $H$ and trial Hamiltonian $H_0$, the free energies obeywhere $F_0=-\log Z_0$ and the expectation uses the trial Gibbs distribution $Z_0^{-1}{e}^{-H_0}$.

The identity $e^{-F}=e^{-F_0}⟨e^{-(H-H_0)}{⟩}_0$ and the Jensen inequality for the convex exponential function give $\log ⟨e^{-(H-H_0)}{⟩}_0\ge -⟨H-H_0{⟩}_0$. Taking minus the logarithm proves the bound.

The harmonic mean of positive numbers $x_1,\dots ,x_n$ is $H=n/({\sum }_{i}1/x_i)$. The Jensen inequality for $1/x$ gives $H\le \overline{x}$, with equality exactly when all entries coincide.

A Borel probability measure is invariant for a Markov kernel semigroup if every transition preserves it. For a Feller semigroup on a compact metric space, it suffices to test the displayed identity on continuous functions. Kernel Jensen inequality gives contraction on $L^p(\mu )$, and sup-norm strong continuity followed by density gives strong continuity there for $1\le p<\infty$.

For a random variable $Z$ and exponents $0<r<s$, the absolute moment norms satisfy $(\mathbb{E}|Z{|}^r{)}^{1/r}\le (\mathbb{E}|Z{|}^s{)}^{1/s}$, allowing infinity. For a finite $s$th moment, apply the Jensen inequality to the concave function $t^{r/s}$ and $|Z{|}^s$. The formula is for positive exponents; $r=0$ requires a separately defined limiting quantity.