The Leibniz rule rewrites the left side as $(xu{)}^{\prime }$. By a distribution with zero derivative is constant,The Dirac delta multiplication identity $x{\delta }_0^{\prime }=-{\delta }_0$ supplies a particular solution, and the preceding division result supplies all solutions of $xu=c$. HenceMultiplying by $x$ and then differentiating verifies the equation. The coordinate-multiplication kernel and the constant-derivative kernel show that no additional solutions are missing.

On a tricritical thermal path, the quartic scaling variable $X$ tends to zero, and at zero field $Y=0$. On the ordered side the tricritical crossover scaling function tends to ${\Phi }_{-}(0,0)=-1/3$. Consequentlyprovided the quadratic coefficient has a nonzero thermal slope and the sextic coefficient has a finite positive limit. Since the heat-capacity critical exponent satisfies $f_{\mathrm{s}}\sim |T-T_c{|}^{2-\alpha }$,Equivalently, twice differentiating this ordered-side free-energy density produces the $(T_c-T{)}^{-1/2}$ singularity. The scaling path and branch qualifications are the same as for the direct Landau theory calculation: a nonzero fixed positive quartic coefficient would instead send $X\to \infty$ and yield the ordinary transition of part (e).

Write $z=(Z_{s_1},\dots ,Z_{s_n}{)}^T$, let ${\Sigma }_{ij}=C(\Vert s_i-s_j\Vert )$, let $c_i=C(\Vert s_i-s_0\Vert )$, and put $v_0=C(0)$. Assume the covariance matrix $\Sigma$ is invertible. Because the mean is known to be zero, every linear predictor $w^{T}z$ is unbiased. Its mean squared prediction error isCompleting the square yieldsThe positive-definite matrix property makes the last term nonnegative, proving the simple kriging formulasThe residual is uncorrelated with $z$. Since the joint law is multivariate normal, it is independent of $z$, so the same predictor and variance describe the conditional distribution. Consequently a 95-percent prediction interval isThere is no constraint that the weights sum to one: that is required for an unknown constant mean in ordinary kriging, not for the stated known-zero-mean problem. If $\Sigma$ is singular, use a Moore-Penrose inverse with the corresponding covariance compatibility condition. At an already observed location prediction of that very same field value has zero error; predicting an independent noisy replicate is a different target. Estimated covariance parameters make this a plug-in interval and generally add uncertainty that the formula does not include.

For EM for zero-inflated negative binomial regression, at iteration $k$ apply Bayes' theorem to each zero in the zero-inflated negative binomial model. The E step computes the conditional expectation of its latent indicator:Treating these values as fixed, the M step maximizes the expected complete-data log-likelihood. Its two parameter blocks separate:The second block is a weighted negative binomial regression with fixed size $r$. Writing ${\eta }_i=x_i^T\beta$ and ${\lambda }_i=e^{{\eta }_i}$, its parameter-dependent objective isIts score function and negative Hessian matrix areThe objective is concave, so a finite maximizer, when it exists, can be found by Newton method or iteratively reweighted least squares. The latter uses working response ${\eta }_i+(y_i-{\lambda }_i)/{\lambda }_i$ and weights $w_{i}r{\lambda }_i/(r+{\lambda }_i)$. This is not an unweighted regression on only the positive counts: zeros retain fractional weight in the count component.

Start with $0<\pi <1$ and finite coefficients, alternate the two steps, and monitor the observed log-likelihood. Exact maximization makes the expectation-maximization algorithm nondecreasing in that likelihood, although the mixture may have multiple stationary points. All-zero data can lead to boundary fits and poor identification; multiple initializations and explicit checks for boundary parameters are useful.

Write $\mathcal{T}(D)$ for the recursively repetition-free labelled trees. Each rooted tree is finite because the inductive definition forms it from an already constructed finite list of finite child rooted trees. Its label list is obtained by visiting the root first, then each child subtree in its prescribed order, using a depth-first traversal of a tree. This list can have repetitions. In particular, different branches may use the same label; the children are required to be distinct rooted trees, not to have distinct root labels.

For any finite label pool $F$, prove by mathematical induction on $|F|$ that $\mathcal{T}(F)$ is finite. For $F=\varnothing$ it is empty. For each root $d\in F$, the children form a finite repetition-free sequence from $\mathcal{T}(F\setminus \{d\})$, which is finite by mathematical induction. There are therefore finitely many child lists for that root, and the finite union over $d\in F$ is finite. More explicitly, if $t_m$ is the number of rooted trees on a pool of $m$ labels, thenThere is no countable choice in this finite mathematical induction.

Now suppose $T_0,T_1,\dots$ were an injective enumeration of a countably infinite subset of $\mathcal{T}(D)$. Their canonical traversal lists give an explicit enumeration of all labels used. If infinitely many different labels occur, the least-first-occurrence procedure gives a countably infinite subset of $D$, a contradiction. Otherwise all labels belong to one finite set $F$. Every $T_n$ then belongs to $\mathcal{T}(F)$: recursively its root lies in $F$ and its child rooted trees use only $F$ with that root removed. But $\mathcal{T}(F)$ is finite by the preceding mathematical induction, again a contradiction. Finally $d\mapsto$ the single-vertex rooted tree labelled $d$ is injective, so $\mathcal{T}(D)$ is infinite. Hence recursively repetition-free labelled trees preserve Dedekind-finiteness:

Let $\mathrm{Inj}(<\omega ,X)$ denote the finite repetition-free sequences, including the empty sequence. The map $x\mapsto (x)$ is injective, so this set is infinite when $X$ is infinite. Suppose, for a contradiction, that it contains a countably infinite subset. Fix its given injective enumeration $s_0,s_1,\dots$; this is part of the supposition, not a choice from a family of sets.

Enumerate all pairs $(n,j)$ with $j<\mathrm{length}(s_n)$ by their natural-number pairing codes, recording the corresponding entries $s_n(j)$. If the union of entries were infinite, recursively selecting the first new entry would give an injection $\mathbb{N}\to X$, contradicting Dedekind-finiteness. Therefore the union is a finite set $F$, say of size $m$. A repetition-free sequence over $F$ has length at most $m$, and there are exactlysuch sequences, with the empty product equal to one. All $s_n$ would belong to this finite set, contradicting their distinctness. Finite counting and the least-code construction use no axiom of choice. This proves finite repetition-free sequences preserve Dedekind-finiteness and the required infinitude:

For the Lie derivative of a tensor field, the scalar rule follows because a vector field $X$ differentiates products of smooth functions. On vector fields the Lie bracket of vector fields satisfiesThis follows by applying both sides to an arbitrary smooth function and expanding the two compositions of derivations. Both the scalar and vector-field operators are real-linear, so the extension theorem applies and gives the unique contraction-compatible tensor operator ${\mathcal{L}}_X$.

For a type-$(1,1)$ tensor $A$, regard it as the pointwise endomorphism of $TM$ obtained by contracting its covector slot with a vector. The endomorphism-induced tensor derivation starts withIt is real-linear and obeys $D_A(fY)=fA(Y)=f{D}_{A}Y+(D_{A}f)Y$. The scalar operator zero is a derivation in every dimension, so there is no zero-dimensional obstruction here. Consequently it also extends uniquely. On a differential one-form, the two operators areTheir actions on arbitrary tensor fields follow by the tensor-product rule; $D_A$ adds $A$ in every contravariant slot and subtracts its dual action in every covariant slot.

Functions in this calculation belong to $C^{\infty }(M)$: the printed $C^{\infty }(X)$ in Q2(c) is a typographical error. The contraction $C_2^1$ pairs the sole covector with the new vector $Y$.