A latent factor is an unobserved coordinate introduced to explain shared variation among measurements. In an orthogonal factor model, latent factors have variance one and are mutually uncorrelated, with coefficients specified by the factor loadings. They differ from a regression factor, which is a categorical predictor. A fitted covariance alone generally leaves the latent factor orientation undetermined; factor rotation gives equivalent representations.

For a finite connected graph embedded as a plane graph, follow the edges around a face of a plane graph to obtain a closed walk. Count traversals, not distinct edges: a bridge in a graph occurs twice along one facial boundary walk. Each edge contributes two sides in total.^[ref]

For the three-vertex graph path $A-B-C$, the only facial boundary walk is $A,B,C,B,A$, of length four. Both edges are bridges in a graph; neither is a graph cycle. Thus a facial boundary walk need not be a graph cycle.

The outer face is the unique unbounded face of a plane graph for a finite plane graph. It counts once even though it surrounds the whole graph drawing. A tree has only this face of a plane graph.

For the threading convention $d{x}^i-N^{i}dt$ and $K_{ij}=-({\dot{\gamma }}_{ij}+D_i{N}_j+D_j{N}_i)/(2N)$, contract with ${\gamma }^{ij}$. Metric compatibility gives ${\gamma }^{ij}{D}_i{N}_j=D_i{N}^i$, and the Jacobi determinant derivative formula gives ${\gamma }^{ij}{\dot{\gamma }}_{ij}={\partial }_t\log \det \gamma$. With zero shift, $-K/3=(3N{)}^{-1}{\partial }_t\log \sqrt{\gamma }$ is the local volume Hubble parameter. Reversing the shift convention reverses both the normal's spatial component and the shift terms; the two conventions must not be mixed.

For a compact regular locale $X$, put $B=\mathcal{O}(X{)}_{\neg \neg }$ and $\mathcal{O}(\gamma X)=\mathrm{Idl}(B)$. This locale is extremally disconnected. Its natural surjection to $X$ has inverse imageThe Joyal extension lemma gives the frame homomorphism; its injectivity follows by recovering $a$ as the join of the regular opens in this ideal.

Map two disjoint walking arrows to the two mutually inverse arrows of the walking isomorphism. Their images generate the entire target, so the functor is an extremal epimorphism. Its kernel pair only identifies the matching endpoint objects, not the inverse-composition relations. Mapping both arrows to a nonidentity idempotent in a one-object category equalizes that kernel pair but cannot factor through the target groupoid. Thus the functor is not a regular epimorphism, although the category of small categories has finite limits.

The characteristic map of a subdivision surface is the planar limit map obtained from the two independent tangent eigenvectors of its local subdivision matrix. In the usual stationary setting, a simple constant eigenvalue one, a semisimple double subdominant eigenvalue $0<\lambda <1$, strictly smaller other eigenvalues, and a regular injective characteristic map establish the standard $C^1$ tangent-plane behavior for nondegenerate data. The eigenvalue inequalities alone do not exclude a folded or singular characteristic map. For an alternating scheme these tests must use a full period of refinement.

The number of coloured neighbours of $v$ minus the number of distinct colours on those neighbours. Repeated colours among pairwise nonadjacent neighbours reduce the number of unavailable colours without increasing the number of neighbours still to colour.

The Lie product formula extends from matrices to any finite-dimensional Lie group. In a local exponential chart, smooth multiplication and its differential give $\log (\exp (sX)\exp (sY))=s(X+Y)+O(s^2)$. Taking $s=t/m$ and using the one-parameter subgroup identity shows that the $m$th power is $\exp (t(X+Y)+O(m^{-1}))$, proving the displayed limit. This proof needs only the first-order expansion, rather than convergence of the full Baker--Campbell--Hausdorff formula.

The Exponential map of a Lie group is a group homomorphism from the additive Lie algebra exactly when its identity component of a Lie group is abelian. If it is additive, its image is an abelian subgroup containing an identity neighborhood by the local exponential chart; connectedness makes that image the entire identity component. Conversely, in an abelian identity component the product $\exp (tX)\exp (tY)$ is a one-parameter subgroup with derivative $X+Y$, giving the identity by uniqueness. The disconnected Lie group $S_3\times \mathbb{R}$ shows that the full group need not be abelian.

The interval required to double a quantity under constant-rate exponential growth. Solving $e^{r{t}_2}=2$ with the natural logarithm gives $t_2=\ln 2/r$. At $r=0.2$ per time unit, it is approximately $3.46574$ time units; every subsequent doubling takes the same interval.

For an exponential dispersion family with a common dispersion parameter $\varphi$, scaled deviance compares a fitted model with the saturated statistical model at the same dispersion:For a normal distribution with variance ${\sigma }^2$, unscaled deviance is the residual sum of squares and scaled deviance is that sum divided by ${\sigma }^2$. For the binomial distribution, $\varphi =1$ and the two coincide. Unknown Gaussian dispersion requires variance estimation; raw deviance differences should not be treated as chi-squared statistics without the scale.

For an impulsive leaky porous gravity current, write $s=(1-e^{-\Omega t})/\Omega$ and $h=(1-\Omega s)[R^2-x^2{]}_{+}/(6Ds)$, where $R=C{s}^{1/3}$. At a fixed lateral location, $h_t=0$ when $x^2=R^2(1+2\Omega s)/3$. The maximum invaded thickness is the envelope $x_{\ast }(s)=C{s}^{1/3}\sqrt{(1+2\Omega s)/3}$, $h_{\ast }(s)=C^2(1-\Omega s{)}^2/(9D{s}^{1/3})$. The source solution is singular at the initial line release; finite source size and aquifer depth limit its near-source validity.

For independent exponential random variables $X,Y$ with the same rate $\lambda >0$, the convolution density of $X+Y/2$ is $2\lambda (e^{-\lambda v}-e^{-2\lambda v})$ for $v>0$. The cumulative distribution function of $\max (X,Y)$ is $(1-e^{-\lambda v}{)}^2$, whose derivative is the same density. This is equality of probability distributions, not an almost-sure equality of the two expressions formed from the same sample.

Let $S$ be a fixed sum of independent exponential random variables with smallest rate $\lambda >0$, and let $Z_N$ be the sum of $N-k$ independent rate-$r$ exponential random variables, independent of $S$, with fixed $k$ and $r>\lambda$. At large-deviation speed $N$, $S/N$ has rate function $\lambda y$ for $y\ge 0$; the sample mean has rate function $K_r(z)=rz-1-\log (rz)$ for $z>0$. The product large-deviation principle and contraction principle for large deviations giveIndeed the derivative of the quantity minimized with respect to $z$ is $r-\lambda -1/z$. Its minimizer is $\min (x,(r-\lambda {)}^{-1})$. Beyond that transition, a single slow exponential random variable carries the extra excursion; the rate function becomes affine.

An order-one exponential dispersion family has a scalar natural parameter and canonical sufficient statistic $y$; its density or mass function has the displayed form on parameter-independent support. Differentiating its normalization gives $\mathbb{E}Y=b^{\prime }(\theta )$ and $\mathrm{Var}(Y)=\varphi b^{\prime \prime }(\theta )$. Known observation weights $w$ replace $\varphi$ by $\varphi /w$. Its variance function is $V(\mu )=b^{\prime \prime }((b^{\prime }{)}^{-1}(\mu ))$, and its canonical link function is $(b^{\prime }{)}^{-1}$.

For an affine autoregressive process $r_T=\beta r_{T-1}+{\xi }_T$, suppose its initial zero-coupon bond prices $q(T)$ are positive and finite. A deterministic shift $h_T$ of the rate changes the bond price to $q(T)\exp (-{\sum }_{s=1}^T{h}_s)$. To fit any positive curve $p(T)$ with $p(0)=1$, put $d_T=\log q(T)-\log p(T)$, $d_0=0$, $h_T=d_T-d_{T-1}$ and $h_0=0$. Adding the deterministic drift ${\alpha }_T=h_T-\beta h_{T-1}$ to the rate recursion realizes precisely this shift, and telescoping gives the desired curve. No stationary autoregression assumption is needed.

A utility indifference price for a claim is the cash amount at which optimally buying the claim leaves the investor with the same maximal expected utility as trading without it.

For an increasing strictly concave utility function satisfying the Inada conditions, put $I=(U^{\prime }{)}^{-1}$ and let $\zeta$ be the positive terminal state-price density. In a finite complete market, select $y>0$ so that $\mathbb{E}[\zeta I(y\zeta )]$ equals initial wealth. The displayed payoff is optimal under that budget: the supporting-line inequality for utility has marginal slope $y\zeta$, and its expected linear error vanishes on the budget constraint. Market completeness supplies its replicating strategy.

If a positive terminal state-price density has essential infimum zero, let $A_n=\{{\zeta }_T<1/n\}$ and $X_n=w_0{1}_{A_n}/\mathbb{E}[{\zeta }_T{1}_{A_n}]$. Whenever $\mathbb{P}(A_n)>0$, these claims cost $w_0$ and satisfy $\mathbb{E}{X}_n>n{w}_0$. In a Brownian complete market with constant nonzero market price of risk, ${\zeta }_T$ has a lognormal distribution and all these events have positive probability. Thus bounded market coefficients and an increasing concave utility do not alone ensure a finite attained optimum: the linear utility $U(x)=x$ already fails. Usual inverse-marginal-utility formulas require further utility and integrability hypotheses.