The Classical Moment Problem And Some Related Questions In Analysis Apr 2026

At first glance, this seems like a straightforward problem of "matching moments." But as we will see, it opens a Pandora's box of deep analysis, touching functional analysis, orthogonal polynomials, complex analysis, and even quantum mechanics. In probability and analysis, a moment is a generalization of the idea of "average power." For a real random variable $X$ with distribution $\mu$ (a positive measure on $\mathbbR$), the $n$-th moment is:

$$ \sum_i,j=0^N a_i a_j m_i+j \ge 0 $$

encodes all the moments. The measure is determinate iff the associated (a tridiagonal matrix) is essentially self-adjoint in $\ell^2$. Indeterminacy corresponds to a deficiency of self-adjoint extensions—a concept from quantum mechanics. Complex Analysis and the Stieltjes Transform Define the Stieltjes transform of $\mu$: At first glance, this seems like a straightforward

We assume all moments exist (are finite). The classical moment problem asks: Given a sequence $(m_n)_n=0^\infty$, does there exist some measure $\mu$ that has these moments? If yes, is that measure unique? If yes, is that measure unique

For the Hamburger problem, this condition is also sufficient (a theorem of Hamburger, 1920): A sequence $(m_n)$ is a Hamburger moment sequence if and only if the Hankel matrix is positive semidefinite. At first glance

$$ x P_n(x) = P_n+1(x) + a_n P_n(x) + b_n P_n-1(x) $$