A closed-population capture-recapture model assumes the same individuals remain throughout the observations: no births, deaths, immigration, or emigration. Its elementary two-sample version additionally requires retained, correctly recognized marks and a second sample chosen by simple random sampling independently of first-sample membership. Mixing and unchanged capture behavior help make this plausible. Equal individual capture probabilities alone do not guarantee that every size-$C$ subset is equally likely; that stronger sampling assumption gives the hypergeometric distribution. See Barker, §§1.1.1 and 1.1.3: closure, marks, and catchability assumptions.

Unequal catchability, mark loss, and population change can invalidate this capture-recapture model. For example, if especially catchable individuals appear repeatedly, overlap can be inflated and the Lincoln-Petersen estimator can underestimate population cardinality. Lost marks can decrease recorded overlap and increase the same ratio estimator, all else equal. Two samples alone cannot establish that the sampling assumptions hold.

In a locally small capital regular category, $\Gamma$ is a regular functor and reflects terminal objects. A well-supported object $A$ has a point: otherwise $A\times A$ is well-supported with no points, so its diagonal is invertible by well-pointedness; then $A$ is subterminal and its regular epimorphism to $1$ is invertible, a contradiction. Pulling a regular epimorphism back along a point gives a well-supported object, so the point lifts. This proves preservation of regular epimorphisms, while representability gives limit preservation. If $\Gamma X$ is a singleton, its unique point $1\to X$ is monic and contains all points; $X$ is well-supported, so that point is invertible. Global sections need not reflect arbitrary isomorphisms between objects of proper support.

Choose one regular functor witnessing the regular-logic separation of a proper subobject for each proper subobject represented by a monomorphism of the small regular category. Their product is regular and reflects invertibility of monomorphisms. If an arbitrary morphism becomes invertible, its image inclusion becomes invertible and so does the diagonal of its kernel pair. The original morphism is both a regular epimorphism and a monomorphism, hence invertible. The target is a power of sets as a capital regular category. This proves an isomorphism-reflecting regular representation without asserting full faithfulness.

Here $I$ is a set regarded as a discrete category. Finite limits and regular epimorphisms are coordinatewise. A well-supported object is a family of nonempty sets. With the axiom of choice, every coordinate element extends to a global point of its product. Any subobject containing all global points must therefore contain every coordinate element, and be the whole family. Thus the category is capital. Families with some empty coordinates explain why global points need not reflect arbitrary isomorphisms.

Adding positive quadratic confinement $t\int h^2/2$ to the membrane energy changes its height spectrum to $k_{B}T/(\sigma q^2+t)$. The continuous height-shift symmetry is explicitly broken. Connected correlation function decay on the finite length $\xi$ and the height-difference variance saturates for every internal dimension, although its saturation value can depend on a microscopic cutoff. A negative confinement coefficient gives an unstable quadratic model rather than pinning.

For $a>0$ and ${\sigma }^2>0$, place probabilitiesTheir weighted deviations cancel, giving expected value $\mu$; their weighted squared deviations give variance ${\sigma }^2$. Only the upper point belongs to $X-\mu \ge a$, so its tail probability attains Cantelli's inequality. With $a=2$, ${\sigma }^2=1$, $\mu =0$, the locations are two and $-1/2$, with masses $1/5$ and $4/5$. The optimized shift makes the lower point zero before squaring, explaining equality in the Markov's inequality step.

The threshold convention matters: this example has $\mathbb{P}(X-\mu >a)=0$. For a strict threshold, placing the upper point at $a+\epsilon$ instead gives probabilities approaching the same bound as $\epsilon \downarrow 0$, with the prescribed mean and variance retained. Extra restrictions on the support of a probability distribution can change which extremal models are allowed.

The difference between the two normalized splitting degrees of the canonical scroll of a trigonal curve measures its imbalance. Since a smooth trigonal curve cannot contain the negative section, its intersection with that section is nonnegative. This gives $3a\le g+2$, and parity gives $a\equiv g(\mathrm{mod}2)$.

Suppose $a^{\prime }$ is bounded and let $d{Z}_t=Z_t{a}^{\prime }(S_t)d{W}_t$, $Z_0=1$. The Novikov condition makes $Z$ a strictly positive true martingale. For ${\pi }_t=U(t,S_t)$ solving the call delta equation for a driftless local volatility diffusion, the product formula givesThe two drift terms cancel through quadratic covariation. Thus $Z\pi$ is a local martingale. If it is a true martingale on the closed horizon, its terminal value is $Z_T{1}_{\{S_T\ge K\}}$, soThe upper bound uses $\mathbb{E}[Z_T\mid {\mathcal{F}}_t]=Z_t$. Equivalently, the option delta is the terminal-exceedance probability under the measure with density $Z_T$.

Solutions of one fixed bounded-coefficient uniformly elliptic equation $Lu=f$ that are bounded in global $L^2$ have a subsequence converging strongly in $H^1$ on every relatively compact subdomain. Local energy bounds and the Rellich-Kondrachov compactness theorem first give local strong $L^2$ and weak $H^1$ convergence on a smooth exhaustion. Pass to the weak equation to identify the limit. Differences solve $L(u_k-u)=0$, so the interior Caccioppoli inequality upgrades the local strong $L^2$ convergence to strong gradient convergence. Global weak $L^2$ convergence preserves the original global bound.

Suppose a compact parameter set carries a locally finite countable multiset of positive quantities, and a fixed injective transform of each labelled quantity is a polynomial in finitely many ambient coordinates. Assume only finitely many labels can have bounded value anywhere in the compact set. Then a uniform finite initial multiset determines the entire multiset. For each source label there are finitely many possible matching labels. Equality for finitely many labels in both directions is a finite union of algebraic sets, obtained from injective matchings and polynomial equalities. These algebraic sets decrease as more labels are included, so they stabilize by the Hilbert basis theorem. A compatible infinite matching in each direction follows by the König infinity lemma, and local finiteness makes multiplicities equal. Squared traces of hyperbolic holonomy representations give the application to the Buser finite length spectrum theorem.

Let the initial dust mass be $m_d$, let its differential number distribution be $K{D}^{-\alpha }$ with $\alpha <4$ between $D_{\min }$ and $D_{\max }$, and put $\eta =D_{\min }/D_{\max }$. Under burst transport under inverse-size radial drag, complete absorption at a fixed radius occurs at $t=TD/D_{\max }$, with $T=t_r(D_{\max })$. The arriving mass is proportional to $D^{3-\alpha }dD$. Substitute $D=D_{\max }t/T$ and $dD/dt=D_{\max }/T$ and use mass normalization of a power-law size distribution to prove the displayed rate for $\eta T\le t\le T$, with zero rate elsewhere. Its integral is exactly $m_d$. Resonant delays, scattering losses, a finite release duration or grain destruction change this ideal pulse.

For initial dust mass $m_d$ in a ring at $r_0$ with a fixed-diameter power-law size distribution of index $\alpha <4$, burst transport under inverse-size radial drag maps radius to diameter $D_r=2\gamma t/(r_0^2-r^2)$. Set $T_r=t_r(D_{\max })$ and $\eta =D_{\min }/D_{\max }$. The surface density is the azimuthal average: $2\pi r\Sigma dr=(\pi \rho K/6)D_r^{3-\alpha }d{D}_r$. Since $d{D}_r/dr=2r{D}_r/(r_0^2-r^2)$, mass normalization of a power-law size distribution gives the displayed expression. It is supported where $\eta \le t/T_r\le 1$. It applies at $t>0$ before removal at the radius in question; the initial state is a delta-function ring. A local angular density needs additional phase information.

The work served in one slot is capped at the service rate of a queue. The remainder is lost, so output is input minus its positive part above capacity. Every downstream threshold below the cap has exactly the same overflow event for input and output; thresholds at or above the cap cannot be exceeded by the output.

For fixed Poisson mean $\lambda >0$ and capacity $c\to \infty$, set $m=\lfloor c\rfloor +1$. The optimized Chernoff bound gives $\log \mathbb{P}(X\ge m)\le -I_P(m)$. The lower bound by $\mathbb{P}(X=m)$ and Stirling's formula differs by only $O(\log m)$. Rounding $m$ to $c$ changes the cost by $O(\log c)$, proving the displayed formula and the leading scale $-c\log c$.

For $u^{\prime }=1-(b+1)u+a{u}^{2}v$, $v^{\prime }=bu-a{u}^{2}v$, the equilibrium $(1,b/a)$ has a Hopf bifurcation at $b=1+a$. Coordinates $x=u-1$, $y=-\sqrt{a}[(u-1)+(v-b/a)]$ give the threshold equations $x^{\prime }=-\sqrt{a}y+(1-a)x^2-2\sqrt{a}xy-a{x}^3-\sqrt{a}{x}^{2}y$, $y^{\prime }=\sqrt{a}x$. In the usual planar radial normalization the cubic coefficient is $-(a+2)/8<0$, proving a nondegenerate supercritical bifurcation. The leading small-amplitude angular frequency is $\sqrt{a}$.

For a deterministic $h>0$, the processes $B_{h-t}-B_h$, $0\le t\le h$, and $B_{h+t}-B_h$, $t\ge 0$, are independent standard Brownian motions. The first uses reversed, sign-changed past increments; the second uses future increments independent of the entire past. The first process is not generally independent of $B_h$, but translating a range by $B_h$ preserves its Lebesgue measure pathwise.

For Brownian motion with drift $dX=dB+\mu dt$ in $(-b,b)$, the displayed probability of exiting at $-b$ follows by stopping the harmonic function $e^{-2\mu X}$. A unit-time increment larger than $2b$ has a uniform positive probability and forces exit, so the exit time has a geometric tail bound from a uniform escape probability and all polynomial moments. At zero drift the limiting probability is $(b-x)/(2b)$.

The process $B_t^k-\mathbb{E}{B}_t^k$ is a martingale only for $k=1,2$. For higher powers its finite-variation drift is $k(k-1)(B_t^{k-2}-\mathbb{E}{B}_t^{k-2})/2$, which is not identically zero. Subtracting the random compensator $k(k-1){\int }_0^t{B}_s^{k-2}ds/2$ instead produces a true martingale on each finite horizon.

// Target: probability-and-statistics.bigb

For standard Brownian motion started at zero, choose an independent radius $R$ with $\mathbb{P}(R\le s)=(s/r{)}^n$, $0\le s\le r$, and stop on first reaching that radius. Rotational invariance of Brownian motion makes the conditional exit position uniform on each sphere. Mixing with radius density $n{s}^{n-1}/r^n$ gives the uniform probability distribution on the ball. In dimension one, the conditional sign has probabilities $1/2,1/2$. The stopped point samples volume measure even though each conditional exit law is supported on a boundary.

For independent coordinates of standard Brownian motion and $x_i>0$, the Brownian reflection principle gives ${\mathbb{P}}_x(T>t)={\prod }_i[2\Phi (x_i/\sqrt{t})-1]$. This is asymptotic to $(2/\pi {)}^{n/2}{\prod }_i{x}_i{t}^{-n/2}$. The tail integral formula for expectation therefore proves the stated criterion, with logarithmic divergence in dimension two.