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For square-integrable complex random variables, the Hermitian covariance is ${\mathrm{Cov}}_{\mathbb{C}}(Z,W)=\mathbb{E}[(Z-\mathbb{E}Z)\overline{(W-\mathbb{E}W)}]$. Its diagonal is nonnegative and equals $\mathbb{E}|Z-\mathbb{E}Z{|}^2$. The conjugation distinguishes it from the pseudo-covariance.

For paired observations $(x_i,y_i)$, the sample correlation coefficient isIt is the normalized inner product of the centered data vectors, or the sample covariance divided by the product of the corresponding sample standard deviations. The choice of divisor $n$ or $n-1$ cancels when used consistently in all three quantities. It is undefined if either centered data vector is zero, which occurs when that variable is constant in the observed statistical sample.

For $x=(1,2,3,4)$ and $y=(2,1,4,3)$, both sample means are $2.5$. The sum of centered products is $3$, and each sum of centered squares is $5$, hence the sample correlation coefficient is $3/5=0.6$. This is a summary of these four observed pairs; it is not automatically the population Pearson correlation coefficient. Philip Stark, Computing the correlation coefficient explains the centered and standardized calculation.

For square-integrable random variables, $\mathrm{Cov}(X,Z\mid \mathcal{G})=\mathbb{E}[XZ\mid \mathcal{G}]-\mathbb{E}[X\mid \mathcal{G}]\mathbb{E}[Z\mid \mathcal{G}]$. It is a $\mathcal{G}$-measurable random variable. Replacing $X$ by its conditional expectation given a larger sigma-field leaves this covariance unchanged when $Z$ is measurable with respect to that larger sigma-field. This observation underlies recovery of a martingale-transform integrand by conditional covariance.

If $Z$ is nondegenerate, $g$ is strictly increasing and the relevant products are integrable, an independent copy $Z^{\prime }$ gives $2\mathrm{Cov}(g(Z),Z)=\mathbb{E}[(g(Z)-g(Z^{\prime }))(Z-Z^{\prime })]$. The integrand is positive exactly when $Z\ne Z^{\prime }$, so the covariance is strictly positive. The same argument applies within each conditional law and yields a positive terminal stock hedge for an attainable increasing payoff.

Countable partial functions from ${\omega }_1$ to a nonempty ground-model set $A$, ordered by extension, add a surjection ${\omega }_1\to A$. Countable closure preserves ${\omega }_1$ and adds no reals.

Conditions are countable successor-length sequences $⟨A_{\xi }\subseteq \xi ⟩$, ordered by end extension. A countable fusion construction decides a named subset below a fresh limit index and writes that trace at the index inside any named club. The generic sequence satisfies the diamond principle.

A Riemannian metric induces a dual inner product on each cotangent space. On decomposable covectors define $⟨{\xi }_1\wedge \cdots \wedge {\xi }_p,{\zeta }_1\wedge \cdots \wedge {\zeta }_p⟩=\det (⟨{\xi }_i,{\zeta }_j⟩)$ and extend bilinearly. Increasing wedges of an orthonormal coframe form an orthonormal basis. This positive-definite inner product is the one used to define the Hodge star operator.

For pressureless matter in Newtonian gauge in cosmology with a common metric potential $\Phi$, let $\theta =\nabla \cdot \mathbf{v}$. The linear equations ${\delta }_m^{\prime }=-\theta +3{\Phi }^{\prime }$ and ${\theta }^{\prime }=-\mathcal{H}\theta -{\nabla }^2\Phi$, where primes denote conformal time, yieldThis follows by differentiating the first equation and eliminating $\theta$. On deeply subhorizon scales with a slowly varying matter potential, the metric time derivatives are small compared with its spatial Laplacian.

In simple hierarchical models, increasing system mass raises the virial temperature and can carry primordial gas beyond its efficient atomic line-cooling interval. At assembly densities, the cooling-time constraint then distinguishes galaxy-sized condensations from larger hot groups or clusters. The resulting characteristic mass is approximate, depends on composition and structure, and does not prohibit subsequent mergers of already formed stellar systems.

Primordial atomic line cooling becomes effective near the temperature where electronic excitations are accessible, of order ten thousand kelvin. Molecular hydrogen has lower-energy transitions and can cool colder gas if it forms and survives. The corresponding virial temperature thresholds translate into epoch-dependent halo mass thresholds. Radiation, molecular chemistry, metal-line cooling and gas density qualify a single universal minimum galaxy mass.

Increasing marginal contributions imply $m_i^{\pi }\ge v((S\cap P_i)\cup \{i\})-v(S\cap P_i)$ for $i\in S$. Summing telescopes to ${\sum }_{i\in S}{m}_i^{\pi }\ge v(S)$, while efficiency follows by telescoping over the full ordering. Hence every marginal contribution vector is in the core of a cooperative game. The core is a convex set, so their average, the Shapley value, is in it too. This gives an elementary proof of nonemptiness and stability for a convex cooperative game.

If a bounded sequence $(y_n)$ in $X$ converges weak-star to $\varphi \in X^{\ast \ast }$ under the canonical embedding into the bidual, then any convex blocks $(u_n)$ have the same scalar limit on every $f\in X^{\ast }$. Thus $y_n-u_n$ is a weakly null sequence, even when $\varphi$ does not belong to the embedded space $X$.

The displayed condition implies convergence in density of a sequence to $\ell$, since the fraction of indices with $|a_n-\ell |\ge \epsilon$ is at most ${\epsilon }^{-2}$ times the averaged squared error. A signed Cesaro limit alone is insufficient: $a_n=(-1{)}^n$ has zero signed average but no density limit.

The global Eddington closure $L=(1-\beta )4\pi cGM/\kappa$, combined with a representative stellar gas-pressure fraction, makes the exterior stellar radiative temperature gradient approximately $M/(4m)$ when the luminosity is constant outside the core. Matching it to the adiabatic temperature gradient estimates the displayed boundary mass. This is a closure estimate: imposing exactly uniform $\beta$ throughout a finite-mass radiative envelope also conflicts with constant luminosity, as described by the constant-beta radiative-envelope obstruction.

Linearize around $A=R{e}^{iqX}$ and write $r=R^2>0$. For sideband wavenumber $l$, the sum and difference of the two amplitude sidebands, together with the field amplitude $b$, evolve underExpanding $\det (\lambda I-M_l)$ givesAt $l=0$, the eigenvalues are $-2r,0,0$; the field's constant mode must be removed if its mean is prescribed. Nonzero long-wave field modes are still dynamically active.

For a real steady amplitude and zero-mean field, twice integrating the field equation yields $B=\kappa (⟨A^2⟩-A^2)$, $\kappa =\mu /\sigma$. The amplitude equation becomes $A^{\prime \prime }+(1-\kappa ⟨A^2⟩)A+(\kappa -1)A^3=0$. Using $(R\mathrm{sech}cX{)}^{\prime \prime }=c^{2}A-2c^2{A}^3/R^2$ gives $\kappa ⟨A^2⟩=1+c^2$ and $R^2=2c^2/(\kappa -1)$, requiring $\kappa >1$. Its finite-window mean is $2R^2\tanh (cL/2)/(cL)$. For $cL\gg 1$, consistency gives $c+c^{-1}=4\kappa /[L(\kappa -1)]$. The leading roots coalesce at $c=1$; for $L>2$ the leading upper parameter limit is $\kappa \le L/(L-2)$, while retaining the finite-window factor makes the necessary inequality strict. The isolated sech profile has an exponentially small derivative mismatch on a periodic interval, so exact periodic tails need correction. For $L\le 2$ the algebra supplies no finite upper bound of this form.

For an entire function of polynomial growth and exponential type $H_K$, multiply it by $\hat{\rho }(\epsilon z)$ with a unit-mass mollifier. The product becomes rapidly decreasing in real frequency, so its inverse Fourier transform has an absolutely convergent contour integral. Shifting in a separating direction forces that smooth inverse to vanish outside $K+\epsilon {\overline{B}}_1$. Letting $\epsilon$ tend to zero recovers a distribution supported in $K$.

Every function $f:\mathbb{R}\to [\mathbb{R}{]}^{<{\omega }_1}$ has reals $x,y$ with $x\notin f(y)$ and $y\notin f(x)$. The values are countable subsets. Requiring distinct witnesses is equivalent, by first adjoining $x$ to $f(x)$. The Freiling theorem identifies this axiom with the negation of the Continuum hypothesis in ZFC.

Place one distinct label at each vertex of a finite connected weighted graph. At rate $c_{uv}=c_{vu}\ge 0$, interchange the labels at $u$ and $v$. This continuous-time Markov chain has a uniform distribution on a finite set as its stationary distribution. The position of one label is the random walk with the same rates.

A jump process with event types $k$, increments ${\nu }_k$, and rates $a_k(X)$ can be represented as $X(t)=X(0)+{\sum }_k{\nu }_k{P}_k({\int }_0^t{a}_k(X(s-))ds)$, with independent unit-rate Poisson processes. This represents the Markov jump-process generator through time-changed counts.