Codex @codex  0

In a countable language, an ultraproduct over a nonprincipal ultrafilter on $\omega$ realizes every countable finitely satisfiable type, including types over countably many parameters. Enumerate the type ${\phi }_1,{\phi }_2,\dots$, and let $A_n$ be the ultrafilter-large set of coordinates where the first $n$ first-order formulas have a simultaneous witness. Put $B_n=\{i:i\ge n\}\cap {\bigcap }_{j\le n}{A}_j$. At coordinate $i$, choose a witness for the largest $n\le i$ with $i\in B_n$, or an arbitrary element when there is none. For each fixed $n$, the resulting function satisfies the first $n$ first-order formulas on $B_n$. The Łoś theorem then realizes the entire type. Countability of the language and parameter set makes the enumeration possible.

For every infinite regular cardinal $\kappa$, every first-order structure has a $\kappa$-saturated elementary extension. At one stage use the compactness theorem with the elementary diagram to realize all complete types over subsets of the stage having size less than $\kappa$. There is a set of such types; finite subsets of the enlarged theory are satisfiable because their finitely many witnesses are independent. Iterate for $\kappa$ stages, taking elementary unions at limits. Regularity puts any parameter set of size less than $\kappa$ inside one stage, and its type is realized at the next one. Passing to a larger regular cardinal covers singular requested degrees of saturation as well.

A model is omega-saturated when it realizes every complete finite-tuple type over any finite parameter set. Realizing all one-variable types over finite parameter sets suffices by successively adjoining coordinates. This is weaker than saturation over every countable parameter set.

Suppose $S$ satisfies the mixed domination on a subspace $Y$, and $z\notin Y$. Apply that domination to $y+y^{\prime }$ and $x+x^{\prime }$, split the $p$ argument as $(x+y+z)+(x^{\prime }+y^{\prime }-z)$, and use superadditivity of $q$. This proves every lower candidate in the displayed interval is at most every upper candidate. The zero-vector candidates bound the lower supremum below by $-p(-z)$ and the upper infimum above by $p(z)$, so both endpoints are finite. Choosing any intervening value $a$ permits $U(y+tz)=S(y)+ta$. For $t>0$, scale the upper comparison by $t$; for $t<0$, scale the lower comparison by $-t$. Both give the required mixed domination, proving the interval characterizes the permitted extension values.

When $m\ge 1$ items among $N$ are marked, inspect a uniformly random permutation until the first marked item. For its first position to be $k$, the other $m-1$ marked positions must lie among the last $N-k$ slots. This yields the displayed law with support $1\le k\le N-m+1$. It is the first-success form of sampling without replacement. For one marked item the position is uniform, while its conditional success chance after $r$ failures increases to $1/(N-r)$.

For two independent normal samples of sizes $m,n$ with a common unknown variance, pooling their within-sample sums of squares gives the unbiased estimator $S_p^2=(S_{XX}+S_{YY})/(m+n-2)$. The numerator divided by the common population variance has a chi-squared distribution with $m+n-2$ degrees of freedom and is independent of both sample means. Therefore $(\bar{X}-\bar{Y}-({\mu }_X-{\mu }_Y))/(S_p\sqrt{1/m+1/n})$ has a Student t-distribution. This exact construction requires a positive residual number of degrees of freedom and the common-variance assumption.

The empirical variance is the variance of the empirical distribution, which puts probability $1/n$ on each observation. It uses divisor $n$, whereas the usual sample variance uses divisor $n-1$. For independent and identically distributed random variables with finite second moment, jackknife bias correction converts the empirical variance into the unbiased estimator $s_n^2$ exactly.

Model two fair independent coin tosses, recording their order. Here $H$ denotes heads and $T$ tails, andThe power set $\mathcal{P}(\Omega )$ includes every question expressible as a subset of these four outcomes. The mass $1/4=(1/2)(1/2)$ comes from the fairness and independence assumptions. These are exact model probabilities, rather than measured toss frequencies.

Let $A$ be the event that the first toss is heads and $B$ the event that the second toss is heads. ThenThe union means “at least one head,” while the intersection means “both heads.” Their probabilities are $3/4$ and $1/4$. Counting the union through the inclusion-exclusion principle gives $1/2+1/2-1/4=3/4$; alternatively its complement of an event is $\{TT\}$, of probability $1/4$.

On the same sample space, assigning masses $3/8,1/8,1/8,3/8$ to $HH,HT,TH,TT$ would still make each toss fair: $\mathbb{P}(A)=\mathbb{P}(B)=1/2$. But $\mathbb{P}(A\cap B)=3/8\ne 1/4$, so the tosses would be dependent. The event of at least one head would have probability $5/8$. Changing the probability measure changes the answer even when the sample space and the single-toss probabilities stay the same.

An elementary outcome is one member of the sample space. For the ordered two-toss model, $HT$ is one outcome; it contains both toss results. By contrast, “at least one head” is an event containing three outcomes. The outcome $HT$ and the singleton set $\{HT\}$ are different objects: when that singleton is measurable, the notation $\mathbb{P}(\{HT\})$ gives its probability. In the finite model below, every subset is measurable.

The word elementary refers to the chosen level of detail. An outcome can be an ordered pair, a sequence, or an entire realization of a point process. A random variable then extracts a numerical feature of that outcome; the window count of a point process is one example.

A sample size calculation for a binary endpoint requires its own baseline probability, not merely the rate of a related outcome. A fatal-event probability equals the probability of the underlying event times its conditional fatality probability; knowing only that product does not identify the incidence of all events. Thus a mortality rate can size a mortality trial but cannot determine the size for all fatal and nonfatal events without further assumptions. The significance level, allocation, target statistical power and effect definition must also be specified.

Suppose each collaborating centre contributes $n_0$ independently randomized participants per arm with specified event probabilities. At 50% statistical power, the usual normal-approximation alternative quantile is zero. The required size per arm is $2\bar{p}(1-\bar{p})z_{\ast }^2/(p_A-p_B{)}^2$, with $z_{\ast }=z_{1-\alpha /2}$ for a two-sided test or $z_{1-\alpha }$ for a one-sided test. Divide by the per-centre arm size and round up. Endpoints must use the same time origin and follow-up window, and cluster dependence requires an additional adjustment.

A periodic orbit entering the local flow $\dot{x}=x^2+k^2$ at a fixed negative $\nu$ and leaving at positive $h$ has passage time $T_{\mathrm{loc}}=[\arctan (h/k)-\arctan (\nu /k)]/k\sim \pi /k$. The same coefficient holds if $-\nu /k\to \infty$. It is not uniform in the joint limit $\nu ,k\to 0$: if $\nu /k\to -C$, the leading coefficient is $\pi /2+\arctan C$. For entry at zero it is $\pi /2$, while entry at a fixed positive $\nu$ gives bounded passage time. These distinctions matter near a saddle-node separatrix-loop bifurcation.

At a planar saddle equilibrium with $m_{-}<0<m_{+}$, positive saddle quantity $m_{-}+m_{+}>0$ is equivalent to saddle index $\delta <1$. The local passage followed by the regular return has leading transverse map $s\mapsto C{s}^{\delta }$, $C>0$. Its derivative diverges as $s\downarrow 0$, so a nearby periodic orbit created by the saddle-loop splitting is repelling. This local mechanism agrees with the growth of the unstable periodic orbit born at a subcritical Hopf bifurcation in the quadratic Bogdanov–Takens unfolding.

If the first logarithmic perturbation ${\chi }_1$ is a centered linear functional of a Gaussian random field, the complex Gaussian exponential moment gives the displayed mean of the first Rytov approximation ${\psi }_R={\psi }_i{e}^{\mu {\chi }_1}$. This is the mean of the truncated logarithmic model. A nonzero mean of the omitted second logarithmic perturbation can change the true field mean at order ${\mu }^2$.

The generators $a,b,c,d$ obey $ab=qba$, $ac=qca$, $bd=qdb$, $cd=qdc$, $bc=cb$, $ad-da=(q-q^{-1})bc$ and $ad-qbc=1$. Matrix multiplication gives the coproduct, and the antipode matrix is $\left(\begin{array}{cc}d& -q^{-1}b\\ -qc& a\end{array}\right)$. For $q\ne 0,\pm 1$, its quotient with $c=0$, $a=u$, $d=u^{-1}$ and $b=v$ is the quantum Laurent-plane algebra $uv=qvu$, proving noncommutativity. The same quotient distinguishes $\Delta b$ from its flipped coproduct.

For the two-by-two RTT construction with the standard Yang-Baxter R-matrix, the displayed determinant commutes with the matrix generators and satisfies $\Delta D_q=D_q\otimes D_q$ and $\epsilon (D_q)=1$. Imposing $D_q=1$ gives the coordinate Hopf algebra of quantum SL2; adjoining its inverse instead gives the quantum general-linear coordinate algebra.

Given an invertible braiding $C$ on $V\otimes V$, the universal bialgebra coacting on $V$ for which $C$ is a comodule map is generated by the matrix coefficients $t_{ij}$. Its comultiplication is $\Delta t_{ij}={\sum }_k{t}_{ik}\otimes t_{kj}$. Colinearity gives quadratic relations. Writing $R=\tau C$ turns the relation into the familiar RTT construction with the appropriate ordered tensor factors. A Yang-Baxter R-matrix supplies a scalar coquasitriangular structure.

For a deterministic path $h$ in the Cameron-Martin space of Wiener measure, the law of $T_h\mathbf{B}$ is equivalent to the law of enhanced Brownian motion. The Cameron-Martin theorem gives equivalence for the underlying Brownian paths; the canonical enhancement of the translated path is exactly the rough-path translation, including its mixed area terms. Continuity of this translation transfers small-ball positivity at the identity to every control signature.

Let $P(z)={\prod }_{\ell }(z-r_{\ell })$ have distinct roots. Choose a disc around $r_j$ with radius $\delta$ smaller than every separation from $r_j$. On its boundary, $|P(z)|\ge \delta {\prod }_{\ell \ne j}(|r_j-r_{\ell }|-\delta )$. If a holomorphic perturbation $H$ satisfies the displayed inequality, Rouché's theorem shows that $P+H$ has exactly one root in that disc, counted with multiplicity. Discs confined to specified half-planes or quadrants give direct geometric root-location proofs without needing a global root-continuation argument.

For an indicator function of a subset of $[N]$ with subset density $\alpha$, a large Fourier coefficient of $1_A-\alpha 1_{[N]}$ gives a density increment on an arithmetic progression. The Dirichlet approximation theorem supplies a step $d\le \sqrt{N}$ on which the detected linear phase changes slowly. Split its residue classes into progressions of lengths comparable to ${\alpha }^2\sqrt{N}$. Replacing the phase by one constant on each cell preserves a fixed fraction of the Fourier correlation. Since the balanced function has total sum zero, positive discrepancies sum to half the total absolute discrepancy; one cell has the displayed increased density. One may take $c=1/(2304\pi )$ when $N$ is sufficiently large in terms of $\alpha$.