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For a real random variable $X$ on a probability space, $\sigma (X)$ is the smallest sigma-algebra making $X$ a measurable function into the Borel sigma-algebra on $\mathbb{R}$. The collection of all inverse images of Borel sets is already a sigma-algebra, since inverse images preserve complements and countable unions; this proves the displayed formula without completing any measure.

A measure is sigma-finite when its space is a countable union of measurable sets of finite measure.

A point measure is a countable sum of Dirac measures. It is simple if all its atoms have mass one, and locally finite if every bounded set has finite mass. Simplicity lets fine enough cells count individual points, whereas occupation indicators would lose multiplicities in a nonsimple measure.

The spherical derivative compares the mass of shrinking centered open balls with their Lebesgue measure, when the limit exists. This is a density of one measure relative to Euclidean volume, rather than the relative density of a set within one fixed measure. A singular finite Borel measure has spherical derivative zero Lebesgue almost everywhere.

For a finite singular Borel measure, choose compact subsets of a Lebesgue-null carrier whose omitted mass is arbitrarily small. Outside any such compact subset, sufficiently small balls see only the omitted measure. The uncentered maximal weak-type inequality bounds the volume of points where the omitted measure has large density. Letting its total mass tend to zero shows that the upper spherical density is zero almost everywhere. This argument does not require the null carrier itself to be closed or separated from every point outside it.

A point process on ${\mathbb{R}}^d$ is stationary if every translation preserves its probability law. If its mean count in a unit cube is finite, its intensity measure is $\mu (A)=\rho |A|$ for some $\rho \ge 0$, where $|A|$ denotes Lebesgue measure. This follows because stationarity makes the mean measure invariant under translations. A constant intensity function of a point process describes only these mean counts, and by itself asserts neither stationarity of the entire law nor independence.^[ref]

For a finite positive measure on the real line, this convention for its Stieltjes transform is analytic off the real line and has positive imaginary part in the upper half-plane if the measure is nonzero. Some sources use $(z-x{)}^{-1}$ instead, changing the sign. The transform of an empirical spectral measure equals the normalized trace of the Stieltjes matrix resolvent.

A transport plan is strongly c-monotone if Borel Kantorovich potentials satisfy the displayed feasible inequality everywhere and equality almost everywhere for the plan. Extended values $-\infty$ away from the full marginal-measure sets may be permitted. This is a potential certificate, not a strict version of every cyclic inequality. A finite continuous cost allows the transport potential path construction.

For a smoothed image signal $u_{\sigma }$, average the gradient outer product as $J_{\rho }=G_{\rho }\ast (\nabla u_{\sigma }\nabla u_{\sigma }^T)$. The dominant eigenvector estimates the normal to a coherent image edge, and eigenvalue contrast measures local directional structure. Two distinct scales separate differentiation from orientation averaging.

For a real function in the $L^{\infty }$ unit ball, push uniform measure on $t\in [0,1]$ forward by the displayed sign-valued functions. In the weak-star topology this is a Borel probability concentrated on the extreme points, and Fubini's theorem shows that its barycenter is $f$. Weak-star compactness and separability of the predual $L^1$ place the example within Choquet theorem.

Consider the probability mass functions $P=(0.4,0.1,0.2,0.3)$ and $Q=(0.1,0.3,0.3,0.3)$ on the finite set $S=\{A,B,C,D\}$. Their difference is $(0.3,-0.2,-0.1,0)$. ThusThe event $E=\{A\}$ attains this difference: $P(E)=0.4$ and $Q(E)=0.1$. Combining $B$ and $C$ gives the same absolute discrepancy with the opposite sign. Adding $D$ to either event changes nothing, because its probability mass agrees under both probability distributions.

The factor $1/2$ is necessary because ${\sum }_x(P(x)-Q(x))=0$: every unit of excess probability mass must match a unit of deficit. For any event, including a negative term cannot increase its signed discrepancy, while omitting a positive term cannot increase it either. Therefore $\{x:P(x)>Q(x)\}$ attains the positive maximum. This is a short way to compute an apparent optimization over $2^{|S|}$ subsets using one pass through $S$.

For probability density functions $p,q$ relative to the same measure $\lambda$, the corresponding identity is $d_{\mathrm{TV}}(P,Q)=\frac{1}{2}\int |p-q|d\lambda$. Such densities can always be taken as Radon-Nikodym derivatives relative to $\lambda =P+Q$. Beware the notation: the total variation norm of a measure applied to the signed measure $P-Q$ equals $2d_{\mathrm{TV}}(P,Q)$. A formula using that norm without its factor $1/2$ is using a different normalization.

The total variation norm of a finite signed or complex measure is the total mass of its variation measure. Under the Riesz-Markov-Kakutani representation theorem, it equals the norm of the corresponding member of $C(K{)}^{\ast }$.

The same compact set must work simultaneously for every measure in the family. Prokhorov's theorem relates this condition to relative compactness for the weak topology of probability measures on a Polish space.

For a finite signed or complex measure $\mu$, its variation is the positive measureIt controls integration by $|\int fd\mu |\le \int |f|d|\mu |$.

A finite-dimensional vector measure has scalar measures as components and a total variation norm of a measure defined using their joint vector norm. Finite variation controls every pairing with a bounded continuous vector test. Derivatives in the bounded-variation space are examples.

A vector Radon measure has Radon measures as components. The vector form of the Riesz-Markov-Kakutani representation theorem identifies finite such measures with bounded functionals on $C_0(\Omega ;{\mathbb{R}}^n)$.

The void probability functional assigns to a measurable set the probability that it contains no atom of a random point measure. For a Poisson random measure of intensity $\mu$, it is $e^{-\mu (A)}$. The exponential void probabilities characterize a Poisson random measure result uses simplicity; doubling every atom of a Poisson measure preserves void probabilities but changes count distributions.

Convergence in the displayed sense combines strong function convergence with weak-star convergence of derivative vector measures against $C_0$ tests. Strong $L^1$ convergence and a uniform variation bound imply the derivative convergence by integration by parts and uniform test-function approximation. Conversely the Uniform boundedness principle bounds the derivative measures of a convergent sequence.

This is the coarsest topology making all displayed bounded-continuous test integrals continuous. On a separable metric space it is metrized by the bounded-Lipschitz metric. This probability-measure topology should be distinguished from the weak topology of a Banach space.

A positive measurable weight $w$ defines the displayed norm on the L2 space for the measure $w(x)dx$, with functions identified when equal almost everywhere. This is the norm induced by the weighted inner product. Changing $w$ changes the contribution of each region to approximation error. If $p_0,\dots ,p_N$ are nonzero mutually orthogonal real functions, completing squares shows that the unique best approximation to real $f$ in their linear span has coefficients $a_j=⟨f,p_j{⟩}_w/\Vert p_j{\Vert }_w^2$.