Positive Gaussian laws and covariance [fdip-0017]
✍️sourceAGENTDRAFTED
- October 20, 2026
Positive Gaussian laws and covariance [fdip-0017]
✍️sourceAGENTDRAFTED
- October 20, 2026
A positive quadratic action weights large fields less heavily. Does its integral over an infinite field space converge, and what expectations does the normalized weight assign? The alternating four-cycle calculation answers both questions explicitly: its 4 precision eigenvalues determine the normalizer and the correlation between opposite vertices. The reference volume is fixed before this weight is chosen.
A positive quadratic action on a finite real inner-product space has a finite Gaussian normalizer. Absolute polynomial moments make its expectation a polynomial state, and the covariance theorem identifies inverse precision as its covariance operator. Zero modes require the separate constraints used for massless laws. Averaging or fixing some fields will later give the distinct marginal and conditional laws.
Lemma 1. A finite real self-adjoint spectral decomposition [fdip-002R]AGENTDRAFTED
- October 25, 2026
Lemma 1. A finite real self-adjoint spectral decomposition [fdip-002R]AGENTDRAFTED
- October 25, 2026
Let \((E,h)\) be a real inner-product space of dimension \(n\geq 0\), and let \(T:E\to E\) be linear and self-adjoint. There is an orthonormal basis \(u_1,\ldots ,u_n\) with real eigenvalues \(q_i\). Put \(P_ix=h(u_i,x)u_i\). Then \[\begin {aligned} I&=\sum _iP_i,&T&=\sum _iq_iP_i,\\ P_iP_j&=\delta _{ij}P_i,&\det T&=\prod _iq_i. \end {aligned}\] Moreover \(h(x,Tx)>0\) for every nonzero \(x\) exactly when every \(q_i>0\). In that case \(T^{-1}=\sum _iq_i^{-1}P_i\) is self-adjoint and positive.
Proof.
- October 25, 2026
Proof.
- October 25, 2026
For \(n>0\), maximize the continuous function \(x\mapsto h(x,Tx)\) on the compact unit sphere. Let \(u\) be a maximizing unit vector and \(q=h(u,Tu)\). If \(v\perp u\), the Rayleigh quotient \[\frac {h(u+tv,T(u+tv))}{1+t^2h(v,v)}\] has derivative zero at \(t=0\). Self-adjointness gives derivative \(2h(v,Tu)\), so \(Tu-qu\) is orthogonal both to \(u^\perp \) and to \(u\). It is zero. Also \(h(u,Tv)=h(Tu,v)=qh(u,v)=0\) on \(u^\perp \), so this complement is stable and the restriction is self-adjoint. Induction gives the basis; the base case \(n=0\) is the empty basis.
Orthonormal expansion proves the projector identities. On the top exterior power, \(\bigwedge ^nT\) multiplies \(u_1\wedge \cdots \wedge u_n\) by \(\prod _iq_i\). This scalar is independent of the nonzero top generator and is the intrinsic determinant. Necessity of positive eigenvalues follows by evaluating at \(u_i\); sufficiency follows from \[h(x,Tx)=\sum _iq_i h(u_i,x)^2.\] Multiplying the projector sums proves the asserted inverse and its positivity. For \(n=0\), the empty product is one and the unique endomorphism is the identity, invertible and vacuously positive.
This gives the operator proof of the real symmetric decomposition in
[boyd2004convex, Appendix A.5.2, pp. 646-647].
Mathlib's LinearMap.IsSymmetric.eigenvectorBasis supplies an
orthonormal eigenbasis for a finite-dimensional inner-product space;
here its scalar field is specialized to \(\mathbb R\). The declaration
alone asserts neither Gaussian normalization nor a graph spectrum.
Definition 2. Positive quadratic actions [fdip-0004]AGENTDRAFTED
- October 16, 2026
Definition 2. Positive quadratic actions [fdip-0004]AGENTDRAFTED
- October 16, 2026
Let \((E,h)\) be a finite-dimensional real inner-product space, with \(n=\dim E\geq 0\), its finite-dimensional topology, Borel sigma-algebra and canonical Euclidean volume. The field specialization is \(E=X_V=\operatorname {Map}(V,\mathbb R)\) for a finite vertex set, including the empty set, with counting inner product \(h_V(x,y)=\sum _{v\in V}x(v)y(v)\) and coordinate Lebesgue measure. There are finitely many coordinates but, in positive dimension, continuously many configurations. Real-spin integration uses this reference convention in [friedli2017statistical, Definition 8.1 and Equation (8.3), author version p. 381]. The extension to \((E,h)\) is the same paired construction in an orthonormal chart; it asserts no ring-general probability law.
A positive precision parameter is a real linear endomorphism \(Q:E\to E\) that is self-adjoint for \(h\) and strictly positive: \(h(x,Qx)>0\) for every \(x\ne 0\). Define its symmetric bilinear form and associated quadratic form by \[b_Q(x,y)=h(x,Qy),\qquad q_Q(x)=b_Q(x,x).\] Linearity in both slots follows from linearity of \(Q\) and the pairing. Self-adjointness gives \(b_Q(y,x)=h(Qy,x)=h(y,Qx)=b_Q(x,y)\). Consequently \[q_Q(x+y)-q_Q(x)-q_Q(y)=2b_Q(x,y).\] Also \(q_Q(cx)=c^2q_Q(x)\) for every real \(c\). This is the quadratic form used here; positivity of its diagonal alone would not assert self-adjointness of an arbitrary endomorphism.
Its Euclidean quadratic action and Boltzmann weight are \[\begin {aligned} S_Q(x)&=\frac 12 q_Q(x)=\frac 12h(x,Qx),\\ w_Q(x)&=\exp (-S_Q(x)). \end {aligned}\] Thus \(q_Q=2S_Q\), \(S_Q(0)=0\), and \(S_Q(x)>0\) off zero. Dividing the polarization identity by two gives \[S_Q(x+y)-S_Q(x)-S_Q(y)=b_Q(x,y).\] The finite-coordinate formulas make \(S_Q\) continuous, hence Borel measurable, and so is its exponential. In particular \(0<w_Q(x)\le 1\), with equality \(w_Q(x)=1\) exactly when \(x=0\). This bound does not prove integrability on a space of infinite reference volume; Gaussian normalization supplies that separate argument.
The counting-array bridge recovers the familiar symmetric matrix and \(x^TQx\) notation from this operator definition. The variables and coefficients here are dimensionless: their chosen scales are absorbed into \(Q\). Wingate's Euclidean convention is \(\exp (-S)\); his lattice-spacing factors belong to his lattice model [wingate2025lattice, Sections 3-4, Equations (3.1) and (4.1)-(4.6), pp. 2-3]. This real nonnegative weight supplies no real-time evolution. The complete four-cycle action gives the concrete operator, array, half factor and test-field value.
Remark 3. The counting array of a precision operator [fdip-002O]AGENTDRAFTED
- October 25, 2026
Remark 3. The counting array of a precision operator [fdip-002O]AGENTDRAFTED
- October 25, 2026
Let \(Q:X_V\to X_V\) be a positive precision parameter. Choose an indicator enumeration and its counting chart \(C:X_V\to \mathbb R^n\). Write \(A=CQC^{-1}\) for the resulting array. Then \[h_V(x,Qy)=(Cx)^TA(Cy).\] Thus \(Q\) is self-adjoint exactly when \(A^T=A\), and is strictly positive exactly when \(z^TAz>0\) for every nonzero \(z\). The action is \(S_Q(x)=\tfrac 12(Cx)^TA(Cx)\).
Proof.
- October 25, 2026
Proof.
- October 25, 2026
The chart sends the indicator basis to the standard orthonormal basis, so the counting pairing becomes the ordinary dot product. Substitution yields the displayed identity. Testing it on every pair of basis vectors proves the symmetry equivalence; bijectivity of \(C\) proves the positivity equivalence. Using this same basis for source and target gives \(\det A=\det Q\); a simultaneous re-enumeration conjugates \(A\) by a permutation matrix and does not change the determinant.
In the ensuing Gaussian formulas, the customary matrix symbol \(Q\) denotes this counting array of the endomorphism. An arbitrary nonorthonormal chart need not make its array symmetric: its Gram matrix must also enter the pairing. This calculation is a coordinate recovery of Definition 2, not a new normalization result. The half-action convention agrees with [wingate2025lattice, Section 4, Equation (4.5), p. 3].
Example 4. The positive action of the alternating cycle [fdip-002P]AGENTDRAFTED
- October 25, 2026
Example 4. The positive action of the alternating cycle [fdip-002P]AGENTDRAFTED
- October 25, 2026
Use the four-cycle oriented \(0\to 1\to 2\to 3\to 0\), with edge weights \((1,t,1,t)\), \(t>0\). Let \(D:X_V\to X_E\) evaluate endpoint differences and \(W\) multiply by the supplied edge weights. With counting adjoint \(D^\dagger \), put \[L=D^\dagger WD,\qquad Q=L+m^2\operatorname {id},\qquad m>0.\] For every \(x,y\), the energy interface gives \[h_V(x,Ly)=\sum _e w(e)(Dx)(e)(Dy)(e).\] It is symmetric in \(x,y\), so \(Q\) is self-adjoint. On the diagonal all edge terms are nonnegative and \(m^2h_V(x,x)>0\) off zero. Thus \(Q\) is a positive precision parameter. At zero mass constant fields instead expose the Laplacian kernel.
In the specified vertex order \((0,1,2,3)\), write \(x=(a,b,c,d)\). The 4 differences are \(b-a,c-b,d-c,a-d\), so the action is exactly \[\begin {aligned} 2S_{t,m}(a,b,c,d) &=(b-a)^2+t(c-b)^2\\ &\quad +(d-c)^2+t(a-d)^2\\ &\quad +m^2(a^2+b^2+c^2+d^2). \end {aligned}\] Each edge occurs once, with one half of its weighted energy in \(S\). Every summand of \(2S\) is nonnegative, with strict positivity off zero from the mass term.
Set \(s=1+t+m^2\). The 4 edge outer products, equivalently expansion of the 4 squares, give diagonal coefficient \(s\), cross terms \(-2ab\), \(-2tbc\), \(-2cd\), \(-2tad\), and no \(ac\) or \(bd\) term. The counting array is therefore \[Q_{t,m}=\begin {pmatrix} s&-1&0&-t\\ -1&s&-t&0\\ 0&-t&s&-1\\ -t&0&-1&s \end {pmatrix}.\] Multiplication recovers \(x^TQ_{t,m}x=2S_{t,m}(x)\). For \(x=(0,1,0,0)\), the edge energy is \(1+t\) and the mass energy is \(m^2\); hence \(S_{t,m}(0,1,0,0)=s/2\), explicitly \(3/2\) when \(t=m=1\). No change of basis or reference measure occurs.
This is a direct calculation in the supplied cycle data, with the edge-once convention of [spielman2009effective, Section 2.1, pp. 4-5] and the positive kinetic sign and half-action of [wingate2025lattice, Section 4, Equation (4.5), p. 3]. All coefficients here are dimensionless. No Gaussian normalizer or covariance is inferred from the energy calculation.
Example 5. Edge energy and the positive action [fdip-001R]AGENTDRAFTED
- October 20, 2026
Example 5. Edge energy and the positive action [fdip-001R]AGENTDRAFTED
- October 20, 2026
For the real alternating four-cycle, take \(t,m>0\) and let \(L_t\) be the weighted Laplacian. Its positive precision parameter is exactly \[Q_{t,m}=L_t+m^2I.\] Indeed, the displayed array of \(L_t\) has diagonal \(1+t\) and the same off-diagonal entries as the action's precision. Adding \(m^2I\) changes that diagonal to \(1+t+m^2\), giving the displayed \(Q_{t,m}\) in Example 4.
For \(f=(a,b,c,d)\), the Euclidean quadratic action is therefore \[\begin {aligned} S_{t,m}(f) &=\frac 12\langle f,L_tf\rangle _V +\frac {m^2}{2}\langle f,f\rangle _V\\ &=\frac 12\bigl ((b-a)^2+t(c-b)^2\\ &\qquad \quad +(d-c)^2+t(a-d)^2\bigr )\\ &\qquad +\frac {m^2}{2}(a^2+b^2+c^2+d^2). \end {aligned}\] This is the same squared-difference formula as in Example 4: half of the edge-once energy plus the mass term. The first summand is nonnegative, and the mass summand is positive for \(f\ne 0\). This calculation compares the graph convention of [spielman2009effective, Section 2.1, pp. 4-5] with the positive kinetic sign and half-action convention of [wingate2025lattice, Section 4, Equation (4.5), p. 3]; the finite four-cycle equality follows by the substitutions above.
Lemma 6. The scalar Gaussian integral [fdip-002S]AGENTDRAFTED
- October 25, 2026
Lemma 6. The scalar Gaussian integral [fdip-002S]AGENTDRAFTED
- October 25, 2026
For real \(a\), consider the extended nonnegative Lebesgue integral \[I(a)=\int _{\mathbb R}e^{-a x^2/2}\,dx.\] For \(a>0\) it is finite and positive, and \[I(a)=\sqrt {2\pi /a},\qquad I(1)^2=2\pi .\] For \(a\leq 0\) it is infinite.
Proof.
- October 25, 2026
Proof.
- October 25, 2026
Suppose \(a>0\). The continuous integrand is at most one on \([-1,1]\). For \(|x|\geq 1\), it is at most \(e^{-a|x|/2}\), whose integral on \(\mathbb R\) is \(4/a\). This proves finiteness before evaluation. On \([-1,1]\) the integrand is at least \(e^{-a/2}\), giving the strictly positive lower bound \(2e^{-a/2}\).
Set \(J=\int _0^\infty e^{-x^2}\,dx\), likewise finite and positive. For each fixed \(y>0\), the substitution \(x=yt\) gives \(J=\int _0^\infty y e^{-y^2t^2}\,dt\). Multiply by \(e^{-y^2}\), integrate in \(y\), and apply nonnegative Tonelli: \[\begin {aligned} J^2&=\int _0^\infty \int _0^\infty y e^{-(1+t^2)y^2}\,dy\,dt\\ &=\int _0^\infty \frac {dt}{2(1+t^2)}=\frac \pi 4. \end {aligned}\] The inner primitive is \(-e^{-(1+t^2)y^2}/(2(1+t^2))\); the final integral is evaluated by arctangent. Thus \(J=\sqrt {\pi }/2\). Reflection and the positive substitution \(x=\sqrt {2/a}\,z\) give the asserted value of \(I(a)\). If \(a\leq 0\), the integrand is at least one everywhere and its nonnegative integral is infinite.
The calculation adapts Keith Conrad's improper-integral argument in
[conradGaussianIntegral, Section 2, Second proof: Another change of variables, PDF pp. 1-2]
to Lebesgue integration. The explicit tail bound and nonnegative Tonelli
justify the adaptation; Tonelli is
[tao2011measure, Theorem 1.7.15, pp. 200-201, preliminary version].
Mathlib's integrable_exp_neg_mul_sq and
integral_gaussian support the real one-variable result with
coefficient \(b=a/2>0\). The value theorem uses a totalized integral;
its value at a nonintegrable parameter does not mean the nonnegative
normalizer is finite.
Theorem 7. Finite positive Gaussian normalization [fdip-002T]AGENTDRAFTED
- October 25, 2026
Theorem 7. Finite positive Gaussian normalization [fdip-002T]AGENTDRAFTED
- October 25, 2026
Let \((E,h)\) be a finite-dimensional real inner-product space, \(n=\dim E\geq 0\), and let \(Q:E\to E\) be self-adjoint. With the canonical h-volume, put locally \[Z=\int _E\exp \bigl (-h(x,Qx)/2\bigr )\,d\lambda _E(x).\] This nonnegative integral is finite if and only if \(Q\) is strictly positive. In that case \[0<Z=\frac {(2\pi )^{n/2}}{\sqrt {\det Q}}<\infty .\] For \(n=0\), the determinant and normalizer are both one.
Proof.
- October 25, 2026
Proof.
- October 25, 2026
Use the orthonormal eigenbasis, with eigenvalues \(q_i\). Its coordinates \(y_i=h(u_i,x)\) preserve canonical volume and give \[\begin {aligned} \frac 12h(x,Qx)&=\frac 12\sum _iq_i y_i^2,\\ \exp \bigl (-h(x,Qx)/2\bigr )&=\prod _i e^{-q_i y_i^2/2}. \end {aligned}\] If \(Q\) is positive, every \(q_i>0\). Repeated nonnegative Tonelli and the scalar integral yield \[Z=\prod _i I(q_i) =\frac {(2\pi )^{n/2}}{\sqrt {\prod _iq_i}}.\] The spectral determinant identity proves the formula. For an empty basis the product and the singleton volume are one.
Conversely, if \(Q\) is not positive, self-adjointness and the spectral lemma give an eigenvalue \(q_i\leq 0\). Restrict the other coordinates to \([-1,1]\). Their product weight is bounded below there by \(c=\exp (-\tfrac 12\sum _{j\ne i}\max (q_j,0))>0\). On the box with \(|y_i|\leq R\), the remaining factor is at least one, so the weight integral is at least \(c\,2^{n-1}(2R)\). These bounds diverge as \(R\to \infty \). This avoids any product of zero and infinity. In dimension zero nonpositivity cannot occur.
For the spectral input, see [boyd2004convex, Appendix A.5.2, pp. 646-647]. For the scalar integral, see Keith Conrad's [conradGaussianIntegral, Section 2, PDF pp. 1-2]. The density convention in [friedli2017statistical, Lemma B.54 and Proposition B.55, pp. 504-505] is a comparison, not an assumption of normalization: that source uses a covariance parameter, while \(Q\) here is precision. For the counting field space, the array bridge recovers the customary determinant formula without changing its reference volume.
Definition 8. The Gaussian partition function and probability law [fdip-0005]AGENTDRAFTED
- October 25, 2026
Definition 8. The Gaussian partition function and probability law [fdip-0005]AGENTDRAFTED
- October 25, 2026
Let \(Q\) be a positive precision parameter on a finite real inner-product space \((E,h)\), with canonical volume \(\lambda _E\). Its Gaussian partition function is \[Z_Q=\int _E w_Q\,d\lambda _E, \qquad w_Q(x)=e^{-h(x,Qx)/2}.\] The normalization theorem proves \[0<Z_Q=\frac {(2\pi )^{n/2}}{\sqrt {\det Q}}<\infty , \qquad n=\dim E.\] Its Gaussian probability law is the Borel measure \[\mu _Q(A)=Z_Q^{-1}\int _A w_Q\,d\lambda _E.\] In the counting field specialization this is exactly the density \(Z_Q^{-1}\exp (-\phi ^TQ\phi /2)\) against the product reference measure.
Proof.
- October 25, 2026
Proof.
- October 25, 2026
The weight is positive Borel. Nonnegativity and the empty-set axiom follow from the nonnegative integral. For pairwise disjoint Borel \(A_k\), the functions \(w_Q\sum _{k=1}^N\mathbf 1_{A_k}\) increase to \(w_Q\mathbf 1_{\bigcup _k A_k}\). Monotone convergence and finite additivity of the integral give countable additivity after division by \(Z_Q\). The total mass is \(Z_Q/Z_Q=1\).
In an orthonormal eigenchart the density is the product of the centered scalar densities \(I(q_i)^{-1}e^{-q_i y_i^2/2}\). This identification uses the scalar value and normalization, before computing covariance. For \(n=0\), the action is zero at the unique point, \(Z_Q=1\), and \(\mu _Q=\delta _0\). Replacing the reference volume by \(c\lambda _E\), \(0<c<\infty \), multiplies \(Z_Q\) by \(c\) and leaves \(\mu _Q\) unchanged, as in the reference-rescaling example. A finite-sum partition function has the same normalizing role. A finite vertex set gives a finite-dimensional configuration space. Integrability of its Gaussian weight follows from the normalization theorem, using positive precision.
The density convention is compared with [friedli2017statistical, Lemma B.54 and Proposition B.55, pp. 504-505]; that source uses covariance rather than precision. Countable additivity uses [tao2011measure, Theorem 1.4.44, pp. 107-108, preliminary version]. The normalization failures distinguish infinite normalizers, zero weights and the zero-dimensional case.
Example 9. Three failures of normalization [fdip-002U]AGENTDRAFTED
- October 25, 2026
Example 9. Three failures of normalization [fdip-002U]AGENTDRAFTED
- October 25, 2026
The normalization theorem distinguishes infinite mass from zero mass. In positive dimension, zero precision has \(S=0\) and weight one, so its integral is the infinite Euclidean volume. Invertibility alone is insufficient: on \(\mathbb R^2\), \(Q=\operatorname {diag}(1,-1)\) has weight \(e^{-x^2/2+y^2/2}\). On \(|x|\leq 1\) the first factor is at least \(e^{-1/2}\), while the integral in \(y\) diverges. Neither weight defines the normalized Gaussian law on that full space.
For a general nonnegative weight \(w\equiv 0\), the integral is zero, so division cannot normalize it. This differs from zero precision. On \(E=0\), the unique endomorphism is invertible and vacuously positive, its Gaussian weight is one, and its normalizer is one. A singular law on a proper subspace requires that subspace's own reference measure; inserting a pseudoinverse does not normalize a divergent ambient density.
These calculations use the spectral and scalar ingredients of Theorem 7. For the scalar distinction, see Keith Conrad's [conradGaussianIntegral, Section 2, PDF pp. 1-2]; the explicit bounds are proved in Lemma 6.
Remark 10. Polynomial functions do not depend on a basis [fdip-002V]AGENTDRAFTED
- October 25, 2026
Remark 10. Polynomial functions do not depend on a basis [fdip-002V]AGENTDRAFTED
- October 25, 2026
For a finite-dimensional real vector space \(E\), the actual polynomial function algebra used in Proposition [fdip-002A] is independent of coordinates. It is the real unital subalgebra of functions \(E\to \mathbb R\) generated by linear functionals. In a chosen basis, every linear functional is a finite linear combination of coordinate evaluations. Conversely every coordinate evaluation is linear. Thus these generators give exactly the coordinate polynomial algebra of that note. Substituting linear combinations in a finite polynomial gives a finite polynomial, so changing basis preserves the algebra.
Its operations and identity involution are pointwise, and its functions are continuous, hence Borel for the finite-dimensional topology. For \(E=0\), the algebra consists of constants. These are actual functions; formal polynomial expressions and their polynomial rings are related algebraic vocabulary, not a second choice of observable functions here.
This coordinate-independence argument is a direct deduction from the generators. The algebra and star conventions are those already used in Proposition [fdip-002A]. For these conventions, see [stacks2017stacks, Section 22.2], Tag 09JF and [waldmann2004states, Definition 3.9, p. 11]. Neither citation asserts Gaussian integrability.
Proposition 11. Every polynomial has finite absolute Gaussian moments [fdip-002W]AGENTDRAFTED
- October 25, 2026
Proposition 11. Every polynomial has finite absolute Gaussian moments [fdip-002W]AGENTDRAFTED
- October 25, 2026
Let \(Q\) be positive self-adjoint on a finite real inner-product space \((E,h)\), with weight \(w_Q=e^{-h(x,Qx)/2}\) and canonical volume. Every actual polynomial function satisfies \[\int _E |P(x)|w_Q(x)\,d\lambda _E(x)<\infty .\] This holds for every finite product and power of polynomials, including \(P^2\), and therefore also for integration against the normalized law.
Proof.
- October 25, 2026
Proof.
- October 25, 2026
First take an integer \(k\geq 0\) and \(a>0\). Put \(c=a/4\), choose an integer \(M\geq k/2\), and set \(C=\max (1,M!c^{-M})\). For \(|x|\geq 1\), the exponential series gives \(e^{cx^2}\geq (cx^2)^M/M!\), hence \[|x|^k e^{-a x^2/2}\leq M!c^{-M} e^{-a x^2/4}.\] For \(|x|\leq 1\), the left side is at most \(e^{-a x^2/2}\), which is at most \(e^{-a x^2/4}\). The global bound therefore holds with \(C\) and includes \(k=M=0\). Its right side is integrable by the scalar lemma with parameter \(a/2\).
In an orthonormal eigenchart, write \[\begin {aligned} P(x)&=\sum _\alpha c_\alpha \prod _i y_i^{\alpha _i},\\ w_Q(x)&=\prod _i e^{-q_i y_i^2/2}. \end {aligned}\] There are finitely many nonnegative-integer multiindices \(\alpha \). Nonnegative Tonelli factors the weighted absolute integral of each monomial into finite scalar integrals just proved. The triangle inequality bounds the polynomial integral by the finite sum of these integrals multiplied by \(|c_\alpha |\). Products and powers remain polynomials, so the same argument applies. In dimension zero each function is constant and its integral is its absolute value at zero.
The envelope estimate and finite-sum argument are direct deductions
from Keith Conrad's [conradGaussianIntegral, Section 2, PDF pp. 1-2] and
[tao2011measure, Theorem 1.7.15, pp. 200-201, preliminary version].
The orthogonal reduction agrees with
[friedli2017statistical, Lemma B.54, p. 504].
TauCeti's TauCeti.integrable_eval_mul_gaussianEnvelope
supports the one-variable polynomial envelope with mean zero and
variance \(w=1/a>0\). The finite products, coordinate substitution and
algebra closure above are separate arguments; no inverse-covariance
identity is used.
Definition 12. Integrable observables and Gaussian expectation [fdip-0006]AGENTDRAFTED
- October 25, 2026
Definition 12. Integrable observables and Gaussian expectation [fdip-0006]AGENTDRAFTED
- October 25, 2026
For the Gaussian probability law \(\mu _Q\) on \((E,h)\), an integrable observable is a Borel function \(F:E\to \mathbb R\) with \(\int _E|F|\,d\mu _Q<\infty \). Its Gaussian expectation is \[\begin {aligned} \omega _Q(F)=\langle F\rangle _Q&=\int _E F\,d\mu _Q\\ &=Z_Q^{-1}\int _E F(x)w_Q(x)\,d\lambda _E(x). \end {aligned}\] The equality follows first for nonnegative simple functions from the density definition, then by monotone convergence, and finally by positive and negative parts for an absolutely integrable function.
These actual functions form a real vector space: the pointwise bound \(|aF+bG|\leq |a||F|+|b||G|\) proves closure, and integration is real linear on it. One may instead take almost-everywhere classes when using \(L^1\); polynomial observables here remain actual functions. All polynomial moments give its polynomial subalgebra; the state proposition proves normalization, positivity and Hermiticity there. The integrable-domain example explains why the full vector space need not be an algebra.
This extends finite expectation: absolute integrability replaces automatic convergence of a finite sum. The real-spin convention is [friedli2017statistical, Definition 8.1 and Equation (8.3), p. 381]; the density-integral extension uses [tao2011measure, Theorem 1.4.44, pp. 107-108, preliminary version]. Constants and oddness give the vanishing linear means. Covariance and variance extend the finite convention of Notation [ftip-008H]; the partition function, two-point expectation and other observable expectations remain distinct quantities.
Proposition 13. Gaussian expectation is a polynomial state [fdip-002X]AGENTDRAFTED
- October 25, 2026
Proposition 13. Gaussian expectation is a polynomial state [fdip-002X]AGENTDRAFTED
- October 25, 2026
For positive self-adjoint \(Q\) on a finite real inner-product space, its expectation restricts to a real algebraic state on the actual polynomial function algebra, with identity involution.
Proof.
- October 25, 2026
Proof.
- October 25, 2026
Absolute polynomial moments make the integral defined on the entire algebra, including every square. Linearity of integration gives real linearity. The probability law gives \(\omega _Q(1)=1\). Positivity follows from \[\omega _Q(P^*P)=\omega _Q(P^2)\geq 0.\] Hermiticity is a separate check: since \(P^*=P\) and conjugation on \(\mathbb R\) is the identity, \(\omega _Q(P^*)=\omega _Q(P)\). More generally every pointwise nonnegative polynomial has nonnegative expectation. In dimension zero this is evaluation at zero on constants.
This is the direct integral realization of the convention in [waldmann2004states, Section 3.1, p. 9; Definitions 3.7 and 3.9, Remark 3.10(iv), p. 11], specialized to ordinary real coefficients as in Definition [fdip-0024]. A supplied unital star-compatible algebra map permits state pullback, in particular the coordinate restriction pullback. This asserts neither an automatic Clifford-algebra state nor a conditional expectation.
Proposition 14. Constants and odd Gaussian observables [fdip-002Y]AGENTDRAFTED
- October 25, 2026
Proposition 14. Constants and odd Gaussian observables [fdip-002Y]AGENTDRAFTED
- October 25, 2026
For the Gaussian expectation associated to positive self-adjoint \(Q\) on \((E,h)\), constants satisfy \(\omega _Q(c)=c\). Every absolutely integrable odd Borel function \(F\), meaning \(F(-x)=-F(x)\), has \(\omega _Q(F)=0\). In particular each linear observable \(\ell _j(x)=h(j,x)\) has mean zero.
Proof.
- October 25, 2026
Proof.
- October 25, 2026
Normalization gives the assertion for constants. Negation is an isometry and preserves canonical volume, by the orthogonal invariance argument in coordinates. Moreover \(h(-x,Q(-x))=h(x,Qx)\), so it preserves the weight. Absolute integrability makes substitution legitimate and gives \[\omega _Q(F)=\omega _Q(F\circ (-I))=-\omega _Q(F).\] Thus the expectation is zero. Linear observables are integrable by Proposition 11. When \(E=0\), an odd function must vanish at its unique point.
This is a direct consequence of normalization and the change-of-variables convention used in [tao2011measure, Exercise 1.2.21, p. 41, preliminary version]. Oddness alone, without absolute integrability, does not define an expectation.
Definition 15. Covariance and variance of Gaussian observables [fdip-002Z]AGENTDRAFTED
- October 25, 2026
Definition 15. Covariance and variance of Gaussian observables [fdip-002Z]AGENTDRAFTED
- October 25, 2026
Let \(\mu _Q\) be the Gaussian law on a finite real inner-product space \((E,h)\), and write \(\omega _Q\) for its expectation. For Borel \(X,Y:E\to \mathbb R\) with \(X\), \(Y\) and \(XY\) absolutely integrable, define their covariance by \[\begin {aligned} \operatorname {Cov}_Q(X,Y) &=\omega _Q(XY)-\omega _Q(X)\omega _Q(Y)\\ &=\omega _Q\bigl ((X-\omega _Q(X))(Y-\omega _Q(Y))\bigr ). \end {aligned}\] Expansion and normalization prove the second equality; all 4 terms are integrable by the stated hypotheses. This extends the finite-sum covariance to integrals.
When \(X\) and \(X^2\) are integrable, its variance is \[\begin {aligned} \operatorname {Var}_Q(X)&=\operatorname {Cov}_Q(X,X)\\ &=\omega _Q\bigl ((X-\omega _Q(X))^2\bigr )\\ &=\omega _Q(X^2)-\omega _Q(X)^2\geq 0. \end {aligned}\] The inequality is positivity of the integral. In particular all these expressions exist for polynomials by Proposition 11, consistently with the polynomial state.
For \(\ell _j(x)=h(j,x)\), set \(K(j,k)=\operatorname {Cov}_Q(\ell _j,\ell _k)\). It is a finite symmetric bilinear form. There is a unique linear covariance operator \(C_Q:E\to E\), relative to the specified \(h\), such that \(h(j,C_Qk)=K(j,k)\).
Proof.
- October 25, 2026
Proof.
- October 25, 2026
In an orthonormal basis put \(C_Qk=\sum _i K(u_i,k)u_i\). Bilinearity of \(K\) follows from integral linearity and gives the linearity of \(C_Q\). Orthonormal expansion proves the required pairing. If \(C_Q'\) has the same pairings, then \(h(j,(C_Q-C_Q')k)=0\) for every \(j,k\); choosing \(j=(C_Q-C_Q')k\) proves equality. Thus the construction is independent of the basis. At \(E=0\) it is the unique map. This representation does not identify \(C_Q\) with inverse precision.
For the counting field space, the coordinate means vanish by Proposition 14, and the former coordinate notation is \[\begin {aligned} \operatorname {Cov}_Q(u,v) &=\left \langle (\phi _u-\langle \phi _u\rangle _Q) (\phi _v-\langle \phi _v\rangle _Q)\right \rangle _Q\\ &=\langle \phi _u\phi _v\rangle _Q. \end {aligned}\] The partition function, this two-point expectation, and the expectation of a different observable are different quantities. The separate inverse-precision theorem computes this covariance.
The definitions extend Notation [ftip-008H] directly; the integral identities use the preceding moment and state results. Positivity uses the ordinary real convention of [waldmann2004states, Definition 3.9, p. 11] through Definition [fdip-0024], not an additional inverse-covariance assumption.
The four-cycle covariance calculation evaluates these quantities for the running example.
Example 16. Integrable observables need not form an algebra [fdip-0030]AGENTDRAFTED
- October 25, 2026
Example 16. Integrable observables need not form an algebra [fdip-0030]AGENTDRAFTED
- October 25, 2026
Let \(Q\) be positive self-adjoint on \((E,h)\) with \(n=\dim E>0\). The Borel function \(F=e^{S_Q/2}\) is integrable under the Gaussian law, since \[\int _E Fw_Q\,d\lambda _E =\int _E e^{-S_Q/2}\,d\lambda _E=Z_{Q/2}<\infty \] by positive normalization. But \(F^2=e^{S_Q}\) is not integrable: \(F^2w_Q=1\), and \(\lambda _E(E)=\infty \). Indeed orthonormal cubes have masses \((2R)^n\) tending to infinity.
Thus actual integrable observables form a vector space but are not closed under multiplication. The same counterexample works for their almost-everywhere classes in \(L^1(\mu _Q)\). The polynomial algebra is an integrable algebra by Proposition 11. For \(n=0\), every real observable is constant on a singleton, so this failure is absent.
This is a direct comparison of the two normalizers, using Theorem 7 and the scalar ingredient in Keith Conrad's [conradGaussianIntegral, Section 2, PDF pp. 1-2]; it also retains \(e^{S_Q}\) as a concrete Borel nonintegrable observable in positive dimension.
Definition 17. Massive graph Green functions [fdip-000A]AGENTDRAFTED
- October 16, 2026
Definition 17. Massive graph Green functions [fdip-000A]AGENTDRAFTED
- October 16, 2026
Let \(V\) be a finite vertex set, with positive edge weights, and let \(L\) be its weighted graph Laplacian. For \(m>0\), define the massive precision and its Green operator by \[Q_m=L+m^2I,\qquad G_m=Q_m^{-1}.\] The inverse exists even when the graph is disconnected. Indeed, the energy identity gives, for \(f\ne 0\), \[\begin {aligned} \langle f,Q_mf\rangle _V &=\sum _e w(e)(Df)(e)^2\\ &\quad +m^2\sum _v f(v)^2>0. \end {aligned}\] Thus \(Q_mf=0\) implies \(f=0\). Finite dimension and rank-nullity make this injective endomorphism surjective, so it is an isomorphism. The inverse-response lemma now gives a self-adjoint inverse and the unique solution \(f=G_mj\) to \(Q_mf=j\). The endomorphism \(Q_m\) is a positive precision parameter; its counting array realizes it as a positive symmetric matrix. For \(j\ne 0\), write \(x=G_mj\ne 0\); then \(\langle j,G_mj\rangle _V=\langle Q_mx,x\rangle _V>0\). On an empty vertex set the same map argument gives the unique isomorphism of zero modules; there are no point-source columns.
By the real spectral theorem, choose an orthonormal eigenbasis \(u_a\) of the self-adjoint operator \(L\), with eigenvalues \(\lambda _a\ge 0\). The energy identity proves this last inequality by evaluation on each unit eigenvector. Put \(q_a=\lambda _a+m^2>0\). Then \[\begin {aligned} Q_m&=\sum _a q_a u_au_a^T,\\ G_m&=\sum _a q_a^{-1}u_au_a^T. \end {aligned}\] Since \(u_a^Tu_b=\delta _{ab}\) and \(\sum _a u_au_a^T=I\), multiplication gives \(Q_mG_m=G_mQ_m=I\). Also \(G_m^T=G_m\), and \(f^TG_mf=\sum _a q_a^{-1}(u_a^Tf)^2>0\) for nonzero \(f\).
The Green kernel is defined from the inverse map by \(G_m(v,w)=(G_m\delta _w)(v)\). These are its matrix entries in the counting coordinate basis. Its column at \(w\) is the unique field solving \[Q_mG_m(\cdot ,w)=\delta _w,\] where \(\delta _w(v)\) is one at \(v=w\) and zero elsewhere. For a source field \(j\), the unique solution of \(Q_mf=j\) is \(f(v)=\sum _wG_m(v,w)j(w)\). No vertex-volume factor occurs because the vertex pairing and basis are counting.
This convention has to be matched when comparing lattice Green functions. Wingate's lattice action uses a positive operator built from minus the backward-forward difference and a mass term [wingate2025lattice, Section 4, Equations (4.2)-(4.5), p. 3]. For unit spacing, that difference term has the sign of our \(L\).
In [friedli2017statistical, Theorem 8.26, p. 399], the massive lattice precision is \[Q=-\frac {1}{2d}\Delta +m^2I.\] Here \(\Delta f(x)=\sum _{y\sim x}(f(y)-f(x))\). The factor \(1/(2d)\) and the lattice boundary conditions belong to that normalization. Identifying their covariance with an inverse requires that precision, not an unscaled adjacency Laplacian.
Mathlib's Matrix.PosDef.isUnit proves invertibility of a
positive definite matrix, and Matrix.PosDef.inv proves
positivity of its inverse. These apply to the matrix step after the
graph-energy calculation supplies positivity. The kernel convention and
its graph interpretation are specified here.
Theorem 18. Covariance is inverse precision [fdip-000B]AGENTDRAFTED
- October 16, 2026
Theorem 18. Covariance is inverse precision [fdip-000B]AGENTDRAFTED
- October 16, 2026
Let \(Q\) be a positive self-adjoint precision on a finite real inner-product space \((E,h)\), with its Gaussian law. For \(j,k\in E\), put \(\ell _j(x)=h(j,x)\). Then \[\begin {aligned} \omega _Q(\ell _j)&=0,\\ \operatorname {Cov}_{\mu _Q}(\ell _j,\ell _k) &=h(j,Q^{-1}k). \end {aligned}\] Thus its covariance operator, relative to the specified pairing, is \(C_Q=Q^{-1}\). On the counting vertex space, \(\ell _{\delta _v}(\phi )=\phi (v)\), so \[\omega _Q(\phi (v)\phi (w)) =h_V(\delta _v,Q^{-1}\delta _w).\] For \(Q=L+m^2I\), this is the Green kernel, with no vertex-volume factor. The density convention agrees with [friedli2017statistical, Proposition B.55, p. 505, and Theorem 8.4, p. 385] after substituting covariance \(Q^{-1}\) for precision \(Q\).
Proof.
- October 16, 2026
Proof.
- October 16, 2026
Write \(g_a(x)=\exp (-a x^2/2)\) and \(I(a)=\int _{\mathbb R}g_a(x)\,dx\) for \(a>0\). The scalar normalization gives \(0<I(a)<\infty \); finite polynomial moments give absolute integrability of \(xg_a(x)\) and \(x^2g_a(x)\). Reflection makes the first integral its own negative, hence zero. On \([-R,R]\), integrate \((xg_a(x))'=g_a(x)-a x^2g_a(x)\) to obtain \[\begin {aligned} a\int _{-R}^R x^2g_a(x)\,dx &=\int _{-R}^R g_a(x)\,dx\\ &\quad -2R\exp (-aR^2/2). \end {aligned}\] For \(R\geq 1\), \(e^{aR^2/2}\geq aR^2/2\) gives \(R e^{-aR^2/2}\leq 2/(aR)\to 0\). Monotone convergence on both nonnegative integrands, with their already established finite integrals, therefore gives \[I(a)^{-1}\int _{\mathbb R}x^2g_a(x)\,dx=a^{-1}.\]
Choose an orthonormal eigenbasis \(u_i\) of \(Q\), with \(Qu_i=q_i u_i\), \(q_i>0\). In \(x=\sum _i y_i u_i\), volume preservation and normalization give the product density \[\prod _i g_{q_i}(y_i)/I(q_i).\] All coordinate products are absolutely integrable by Proposition 11, so signed Fubini applies. Integrating the other coordinates gives one. For \(i\ne j\), the two first moments give zero; for \(i=j\), the scalar second moment gives \(q_i^{-1}\). Thus \(\omega _Q(y_i y_j)=\delta _{ij}q_i^{-1}\) and every \(\omega _Q(y_i)=0\). The finite expansion \(\ell _j(x)=\sum _i h(j,u_i)y_i\) now yields \[\begin {aligned} \operatorname {Cov}(\ell _j,\ell _k) &=\sum _i q_i^{-1}h(j,u_i)h(k,u_i)\\ &=h(j,Q^{-1}k). \end {aligned}\] If two operators represented this bilinear form, their difference applied to \(k\) would be orthogonal to every \(j\), hence zero. This proves uniqueness. In dimension zero the covariance is the zero form, and its representing map is the unique invertible endomorphism.
For a nonorthonormal basis \(b_i\), the coordinate functional is \(h(b^i,\cdot )\). The dual basis satisfies \(h(b^i,b_j)=\delta _{ij}\). Its covariance array therefore has entries \(h(b^i,Q^{-1}b^j)\); it is not the unqualified array of the inverse operator in that basis. The counting basis is orthonormal, giving the coordinate formula above. The alternating-cycle example evaluates it explicitly.
To compare with formal Gaussian providers, put \(n=\dim E\), choose an orthonormal
chart and let \(S\) represent \(Q^{-1}\) there. The positive inverse
is positive definite, \(S^{-1}\) represents \(Q\), and
\(\det S=(\det Q)^{-1}\). Consequently the provider density is
\[\begin {aligned}
&(2\pi )^{-n/2}(\det S)^{-1/2}
e^{-y^TS^{-1}y/2}\\
&\qquad =Z_Q^{-1}e^{-h(x,Qx)/2}.
\end {aligned}\]
The orthonormal chart and the Euclidean-space coordinate
wrapper preserve canonical volume. Thus the chart pushforward of
\(\mu _Q\) is \(\mathrm {multivariateGaussian}(0,S)\) by
TauCeti.Probability.multivariateGaussian_eq_withDensity.
Mathlib's
ProbabilityTheory.covariance_eval_multivariateGaussian
then identifies its coordinate covariance with \(S\); its positive
semidefinite hypothesis follows from positive definiteness. The
parameter is covariance \(S=Q^{-1}\), not precision \(Q\). This density,
determinant and volume identification is needed in addition to the
covariance declaration; the integral proof above is independent of it.
Example 19. Exact covariance on the alternating four-cycle [fdip-0037]AGENTDRAFTED
- October 29, 2026
Example 19. Exact covariance on the alternating four-cycle [fdip-0037]AGENTDRAFTED
- October 29, 2026
Take the four-cycle on \(V=(0,1,2,3)\), with forward edges and weights \((1,t,1,t)\), \(t>0\). Use counting pairing and \(Q=L+m^2I\), \(m>0\). Put \(a=m^2\) and \(s=1+t+a\). The following functions on vertices form an orthonormal eigenbasis; the middle column lists the unnormalized rows \(2u_i^T\): \[\begin {array}{c|c|c} i&2u_i^T&\lambda _i\\\hline 0&(1,1,1,1)&0\\ 1&(1,-1,-1,1)&2\\ 2&(1,1,-1,-1)&2t\\ 3&(1,-1,1,-1)&2+2t \end {array}\]
Proof.
- October 29, 2026
Proof.
- October 29, 2026
Each row has squared norm 4. Distinct rows agree in two signs and disagree in two, giving inner product zero. The weighted coordinate formula is \((L\phi )_0=(1+t)\phi _0-\phi _1-t\phi _3\), with the other rows given by their adjacent weights. For \(v_i=2u_i\), all 4 products are \[\begin {aligned} Lv_0&=(0,0,0,0),\\ Lv_1&=(2,-2,-2,2),\\ Lv_2&=(2t,2t,-2t,-2t),\\ Lv_3&=(2+2t,-2-2t,\\ &\qquad 2+2t,-2-2t). \end {aligned}\] For example the zeroth coordinates are respectively \(0\), \((1+t)+1-t=2\), \((1+t)-1+t=2t\), and \((1+t)+1+t=2+2t\); the same substitutions at the other three vertices give the displayed signs. Thus \(Lu_i=\lambda _i u_i\). This basis remains orthonormal at \(t=1\), when \(\lambda _1=\lambda _2\); no distinct-eigenvalue assumption is needed.
Set \[\begin {aligned} q_0&=a,\\ q_1&=a+2,\\ q_2&=a+2t,\\ q_3&=a+2+2t,\\ \Delta &=a(a+2)(a+2t)(a+2+2t). \end {aligned}\] All 4 \(q_i\) are positive. With \(P_{u_i}=u_i u_i^T\), normalization and inverse covariance give \[\begin {aligned} \det Q&=\Delta ,& Z_Q&=4\pi ^2/\sqrt \Delta ,\\ G&=\sum _{i=0}^3q_i^{-1}P_{u_i}. \end {aligned}\] With \(O\) the array whose columns are \(u_i\), this is \(G=O\operatorname {diag}(q_i^{-1})O^T\).
Write \(d_i=q_i^{-1}\). Multiplying the signs of the relevant coordinates in each projector gives \[\begin {aligned} 4G_{00}&=d_0+d_1+d_2+d_3,\\ 4G_{01}&=d_0-d_1+d_2-d_3,\\ 4G_{02}&=d_0-d_1-d_2+d_3,\\ 4G_{03}&=d_0+d_1-d_2-d_3. \end {aligned}\] Thus the complete covariance, with \((A,B,C,D)=(G_{00},G_{01},G_{02},G_{03})\), is \[G=\begin {pmatrix} A&B&C&D\\B&A&D&C\\C&D&A&B\\D&C&B&A \end {pmatrix}.\] In particular the variance and adjacent two-point expectation are the first two formulas, with their indicated signs.
For the opposite entry, the common denominator \(4\Delta \) has numerator \[\begin {aligned} N&=(a+2)(a+2t)(a+2+2t)\\ &\quad -a(a+2t)(a+2+2t)\\ &\quad -a(a+2)(a+2+2t)\\ &\quad +a(a+2)(a+2t)\\ &=2[(a+2t)(a+2+2t)-a(a+2)]\\ &=8t(a+1+t). \end {aligned}\] Consequently \(G_{02}=2ts/\Delta \). At \((t,m)=(1,1)\), \(q=(1,3,3,5)\), \(\Delta =45\), \(Z_Q=4\pi ^2/(3\sqrt 5)\), and \[\begin {aligned} &(G_{00},G_{01},G_{02},G_{03})\\ &\qquad =(7/15,1/5,2/15,1/5). \end {aligned}\] At \((t,m)=(2,1)\), \(q=(1,3,5,7)\), \(\Delta =105\), and \[(G_{00},G_{01},G_{02},G_{03}) =(44,19,16,26)/105.\] These numbers are obtained by direct substitution in the 4 signed sums above. They give, in particular, the opposite covariance used in block elimination and the two distinct adjacent covariances used in relabeling.
More generally, weights \((b,c,b,c)\) with \(b,c>0\) keep the same 4 eigenvectors and give eigenvalues \(0,2b,2c,2b+2c\), by the same coordinate products. This is a real spectral calculation. The arithmetic columns are \(2u_i\), whose Gram matrix is \(4I\); they are not an orthonormal basis over an arbitrary coefficient ring.
The eigenvectors and signed sums are computed here from the weighted cycle.
[boyd2004convex, Section A.5.2, pp. 646-647] gives general spectral decomposition.
[friedli2017statistical, Proposition B.55, p. 505] gives the Gaussian density. Covariance equals inverse precision by Theorem 18.