Isserlis' theorem

Last updated November 04, 2024

In probability theory, Isserlis' theorem or Wick's probability theorem is a formula that allows one to compute higher-order moments of the multivariate normal distribution in terms of its covariance matrix. It is named after Leon Isserlis.

Statement

If $(X_{1},\dots ,X_{n})$ is a zero-mean multivariate normal random vector, then $\operatorname {E} [\,X_{1}X_{2}\cdots X_{n}\,]=\sum _{p\in P_{n}^{2}}\prod _{\{i,j\}\in p}\operatorname {E} [\,X_{i}X_{j}\,]=\sum _{p\in P_{n}^{2}}\prod _{\{i,j\}\in p}\operatorname {Cov} (\,X_{i},X_{j}\,),$ where the sum is over all the pairings of $\{1,\ldots ,n\}$ , i.e. all distinct ways of partitioning $\{1,\ldots ,n\}$ into pairs $\{i,j\}$ , and the product is over the pairs contained in $p$ .^[5]^[6]

More generally, if $(Z_{1},\dots ,Z_{n})$ is a zero-mean complex-valued multivariate normal random vector, then the formula still holds.

The expression on the right-hand side is also known as the hafnian of the covariance matrix of $(X_{1},\dots ,X_{n})$ .

Odd case

If $n=2m+1$ is odd, there does not exist any pairing of $\{1,\ldots ,2m+1\}$ . Under this hypothesis, Isserlis' theorem implies that $\operatorname {E} [\,X_{1}X_{2}\cdots X_{2m+1}\,]=0.$ This also follows from the fact that $-X=(-X_{1},\dots ,-X_{n})$ has the same distribution as $X$ , which implies that $\operatorname {E} [\,X_{1}\cdots X_{2m+1}\,]=\operatorname {E} [\,(-X_{1})\cdots (-X_{2m+1})\,]=-\operatorname {E} [\,X_{1}\cdots X_{2m+1}\,]=0$ .

Even case

In his original paper,^[7] Leon Isserlis proves this theorem by mathematical induction, generalizing the formula for the $4^{\text{th}}$ order moments,^[8] which takes the appearance

\operatorname {E} [\,X_{1}X_{2}X_{3}X_{4}\,]=\operatorname {E} [X_{1}X_{2}]\,\operatorname {E} [X_{3}X_{4}]+\operatorname {E} [X_{1}X_{3}]\,\operatorname {E} [X_{2}X_{4}]+\operatorname {E} [X_{1}X_{4}]\,\operatorname {E} [X_{2}X_{3}].

If $n=2m$ is even, there exist $(2m)!/(2^{m}m!)=(2m-1)!!$ (see double factorial) pair partitions of $\{1,\ldots ,2m\}$ : this yields $(2m)!/(2^{m}m!)=(2m-1)!!$ terms in the sum. For example, for $4^{\text{th}}$ order moments (i.e. $4$ random variables) there are three terms. For $6^{\text{th}}$ -order moments there are $3\times 5=15$ terms, and for $8^{\text{th}}$ -order moments there are $3\times 5\times 7=105$ terms.

Example

We can evaluate the characteristic function of gaussians by the Isserlis theorem: $E[e^{-iX}]=\sum _{k}{\frac {(-i)^{k}}{k!}}E[X^{k}]=\sum _{k}{\frac {(-i)^{2k}}{(2k)!}}E[X^{2k}]=\sum _{k}{\frac {(-i)^{2k}}{(2k)!}}{\frac {(2k)!}{k!2^{k}}}E[X^{2}]^{k}=e^{-{\frac {1}{2}}E[X^{2}]}$

Proof

Since both sides of the formula are multilinear in $X_{1},...,X_{n}$ , if we can prove the real case, we get the complex case for free.

Let $\Sigma _{ij}=\operatorname {Cov} (X_{i},X_{j})$ be the covariance matrix, so that we have the zero-mean multivariate normal random vector $(X_{1},...,X_{n})\sim N(0,\Sigma )$ . Since both sides of the formula are continuous with respect to $\Sigma$ , it suffices to prove the case when $\Sigma$ is invertible.

Using quadratic factorization $-x^{T}\Sigma ^{-1}x/2+v^{T}x-v^{T}\Sigma v/2=-(x-\Sigma v)^{T}\Sigma ^{-1}(x-\Sigma v)/2$ , we get

${\frac {1}{\sqrt {(2\pi )^{n}\det \Sigma }}}\int e^{-x^{T}\Sigma ^{-1}x/2+v^{T}x}dx=e^{v^{T}\Sigma v/2}$

Differentiate under the integral sign with $\partial _{v_{1},...,v_{n}}|_{v_{1},...,v_{n}=0}$ to obtain

$E[X_{1}\cdots X_{n}]=\partial _{v_{1},...,v_{n}}|_{v_{1},...,v_{n}=0}e^{v^{T}\Sigma v/2}$ .

That is, we need only find the coefficient of term $v_{1}\cdots v_{n}$ in the Taylor expansion of $e^{v^{T}\Sigma v/2}$ .

If $n$ is odd, this is zero. So let $n=2m$ , then we need only find the coefficient of term $v_{1}\cdots v_{n}$ in the polynomial ${\frac {1}{m!}}(v^{T}\Sigma v/2)^{m}$ .

Expand the polynomial and count, we obtain the formula. $\square$

Generalizations

Gaussian integration by parts

An equivalent formulation of the Wick's probability formula is the Gaussian integration by parts. If $(X_{1},\dots X_{n})$ is a zero-mean multivariate normal random vector, then

$\operatorname {E} (X_{1}f(X_{1},\ldots ,X_{n}))=\sum _{i=1}^{n}\operatorname {Cov} (X_{1},X_{i})\operatorname {E} (\partial _{X_{i}}f(X_{1},\ldots ,X_{n})).$ This is a generalization of Stein's lemma.

The Wick's probability formula can be recovered by induction, considering the function $f:\mathbb {R} ^{n}\to \mathbb {R}$ defined by $f(x_{1},\ldots ,x_{n})=x_{2}\ldots x_{n}$ . Among other things, this formulation is important in Liouville conformal field theory to obtain conformal Ward identities, BPZ equations ^[9] and to prove the Fyodorov-Bouchaud formula.^[10]

Non-Gaussian random variables

For non-Gaussian random variables, the moment-cumulants formula^[11] replaces the Wick's probability formula. If $(X_{1},\dots X_{n})$ is a vector of random variables, then $\operatorname {E} (X_{1}\ldots X_{n})=\sum _{p\in P_{n}}\prod _{b\in p}\kappa {\big (}(X_{i})_{i\in b}{\big )},$ where the sum is over all the partitions of $\{1,\ldots ,n\}$ , the product is over the blocks of $p$ and $\kappa {\big (}(X_{i})_{i\in b}{\big )}$ is the joint cumulant of $(X_{i})_{i\in b}$ .

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