Stein and the Normal Distribution

Hello and happy 2021!

Inspired from this tweet, I wanted to understand the basics of Stein’s characterization of the Normal distribution.

With Stein’s idea, we can identify the distribution of a random variable by checking that it satisfies some condition in expectation. For example, for the standard normal, we have that \(X\sim N(0,1)\) if and only if for all \(f\) with \(E[f']<\infty\) we have:


The operator \(Af:=xf-f'\) is then called the Stein operator and we can rewrite the result with this operator as: \(\mathbb{E}_{P}[Af]=0\) for all \(f\in C^1_b\) iff \(P\) is \(N(0,1)\).

This operator is not unique as we can always add P-measure zero parts; see \(Bf:=xf-f'+x\) which satisfies


How can we show \(A\) characterises the standard Normal? A key identity is that the probability density function (PDF) of the \(N(0,1)\) satisfies \(P'+xP=0\). So, if \(P\) is indeed the PDF of the standard normal, then applying integration by parts to \(E_{P}[f']\) and using the differential equation gives us Stein’s formula.

Now, if \(P\) is the PDF of any other distribution and it satisfies Stein’s formula for all \(f\in C^1_b\), then integration by parts on \(E_{P}[f']\) leads us back to the \(P'+xP=0\). That ODE is separable with solution \(P\propto \exp(-x^2/2)\), which, up to the normalisation, is the PDF of the standard normal!

This strategy of deriving an ODE for the density function, getting its weak form by multiplying with a smooth function \(f\) and integrating can be repeated to get Stein operators for other distributions, e.g., the exponential, etc.