On algebraic Stein operators for Gaussian polynomials

TL;DR

This paper introduces algebraic Stein operators for Gaussian polynomial targets, linking Stein's method with computational algebra, and provides a MATLAB tool to find all such operators up to certain degrees and orders.

Contribution

It develops a novel algebraic method to identify all algebraic Stein operators for Gaussian polynomial transformations, connecting Stein's method with null controllability of linear systems.

Findings

Provides a MATLAB code for null controllability checks

Finds Stein operators for Hermite polynomial distributions

First to connect Stein's method with computational algebra for complex distributions

Abstract

The first essential ingredient to build up Stein's method for a continuous target distribution is to identify a so-called \textit{Stein operator}, namely a linear differential operator with polynomial coefficients. In this paper, we introduce the notion of \textit{algebraic} Stein operators (see Definition \ref{def:algebraic-Stein-Operator}), and provide a novel algebraic method to find \emph{all} the algebraic Stein operators up to a given order and polynomial degree for a target random variable of the form $Y=h(X)$, where $X=(X_1,\dots, X_d)$ has i.i.d$.$ standard Gaussian components and $h\in \mathbb{K}[X]$ is a polynomial with coefficients in the ring $\mathbb{K}$. Our approach links the existence of an algebraic Stein operator with \textit{null controllability} of a certain linear discrete system. A \texttt{MATLAB} code checks the null controllability up to a given finite time $T$ (the order of the differential operator), and provides all \textit{null control} sequences (polynomial coefficients of the differential operator) up to a given maximum degree $m$. This is the first paper that connects Stein's method with computational algebra to find Stein operators for highly complex probability distributions, such as $H_{20}(X_1)$, where $H_p$ is the $p$-th Hermite polynomial. Some examples of Stein operators for $H_p(X_1)$, $p=3,4,5,6$, are gathered in the Appendix and many other examples are given in the Supplementary Information.