Definition. The Gaussian partition function and probability law [fdip-0005]

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.

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.