Assume that X₁, ..., X_k are random variables with finite moments. The Wick product

${\displaystyle \langle X_{1},\dots ,X_{k}\rangle \,}$ 

is a sort of product defined recursively as follows:^{[citation needed]}

${\displaystyle \langle \rangle =1\,}$ 

(i.e. the empty product—the product of no random variables at all—is 1). For k ≥ 1, we impose the requirement

${\displaystyle {\partial \langle X_{1},\dots ,X_{k}\rangle \over \partial X_{i}}=\langle X_{1},\dots ,X_{i-1},{\widehat {X}}_{i},X_{i+1},\dots ,X_{k}\rangle ,}$ 

where ${\displaystyle {\widehat {X}}_{i}}$  means that X_i is absent, together with the constraint that the average is zero,

${\displaystyle \operatorname {E} \langle X_{1},\dots ,X_{k}\rangle =0.\,}$ 

Equivalently, the Wick product can be defined by writing the monomial ${\displaystyle X_{1}\dots X_{k}}$  as a "Wick polynomial":

${\displaystyle X_{1}\dots X_{k}=\sum _{S\subseteq \left\{1,\dots ,k\right\}}\operatorname {E} \left(\textstyle \prod _{i\notin S}X_{i}\right)\cdot \langle X_{i}:i\in S\rangle \,}$ ,

where ${\displaystyle \langle X_{i}:i\in S\rangle }$  denotes the Wick product ${\displaystyle \langle X_{i_{1}},\dots ,X_{i_{m}}\rangle }$  if ${\displaystyle S=\left\{i_{1},\dots ,i_{m}\right\}}$ . This is easily seen to satisfy the inductive definition.

It follows that

${\displaystyle \langle X\rangle =X-\operatorname {E} X,\,}$ 

${\displaystyle \langle X,Y\rangle =XY-\operatorname {E} Y\cdot X-\operatorname {E} X\cdot Y+2(\operatorname {E} X)(\operatorname {E} Y)-\operatorname {E} (XY),\,}$ 

${\displaystyle {\begin{aligned}\langle X,Y,Z\rangle =&XYZ\\&-\operatorname {E} Y\cdot XZ\\&-\operatorname {E} Z\cdot XY\\&-\operatorname {E} X\cdot YZ\\&+2(\operatorname {E} Y)(\operatorname {E} Z)\cdot X\\&+2(\operatorname {E} X)(\operatorname {E} Z)\cdot Y\\&+2(\operatorname {E} X)(\operatorname {E} Y)\cdot Z\\&-\operatorname {E} (XZ)\cdot Y\\&-\operatorname {E} (XY)\cdot Z\\&-\operatorname {E} (YZ)\cdot X\\&-\operatorname {E} (XYZ)\\&+2\operatorname {E} (XY)\operatorname {E} Z+2\operatorname {E} (XZ)\operatorname {E} Y+2\operatorname {E} (YZ)\operatorname {E} X\\&-6(\operatorname {E} X)(\operatorname {E} Y)(\operatorname {E} Z).\end{aligned}}}$ 

In the notation conventional among physicists, the Wick product is often denoted thus:

${\displaystyle :X_{1},\dots ,X_{k}:\,}$ 

and the angle-bracket notation

${\displaystyle \langle X\rangle \,}$ 

is used to denote the expected value of the random variable X.

The nth Wick power of a random variable X is the Wick product

${\displaystyle X'^{n}=\langle X,\dots ,X\rangle \,}$ 

with n factors.

The sequence of polynomials P_n such that

${\displaystyle P_{n}(X)=\langle X,\dots ,X\rangle =X'^{n}\,}$ 

form an Appell sequence, i.e. they satisfy the identity

${\displaystyle P_{n}'(x)=nP_{n-1}(x),\,}$ 

for n = 0, 1, 2, ... and P₀(x) is a nonzero constant.

For example, it can be shown that if X is uniformly distributed on the interval [0, 1], then

${\displaystyle X'^{n}=B_{n}(X)\,}$ 

where B_n is the nth-degree Bernoulli polynomial. Similarly, if X is normally distributed with variance 1, then

${\displaystyle X'^{n}=H_{n}(X)\,}$ 

where H_n is the nth Hermite polynomial.

${\displaystyle (aX+bY)^{'n}=\sum _{i=0}^{n}{n \choose i}a^{i}b^{n-i}X^{'i}Y^{'{n-i}}}$ 

${\displaystyle \langle \operatorname {exp} (aX)\rangle \ {\stackrel {\mathrm {def} }{=}}\ \sum _{i=0}^{\infty }{\frac {a^{i}}{i!}}X^{'i}}$ 

• Wick Product Springer Encyclopedia of Mathematics
• Florin Avram and Murad Taqqu, (1987) "Noncentral Limit Theorems and Appell Polynomials", Annals of Probability, volume 15, number 2, pages 767—775, 1987.
• Hida, T. and Ikeda, N. (1967) "Analysis on Hilbert space with reproducing kernel arising from multiple Wiener integral". Proc. Fifth Berkeley Sympos. Math. Statist. and Probability (Berkeley, Calif., 1965/66). Vol. II: Contributions to Probability Theory, Part 1 pp. 117–143 Univ. California Press
• Wick, G. C. (1950) "The evaluation of the collision matrix". Physical Rev. 80 (2), 268–272.