The Classical Moment Problem And Some Related Questions In Analysis -

for all finite sequences $(a_0,\dots,a_N)$. This means the infinite $H = (m_i+j)_i,j=0^\infty$ must be positive semidefinite (all its finite leading principal minors are $\ge 0$).

$$ S(z) = \int_\mathbbR \fracd\mu(x)x - z, \quad z \in \mathbbC\setminus\mathbbR $$ for all finite sequences $(a_0,\dots,a_N)$

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: In probability and analysis, a moment is a

Imagine you are given a mysterious black box. You cannot see inside it, but you are allowed to ask for specific "moments." You ask: "What is the average position?" The box replies: $m_1 = 0$. You ask: "What is the average squared position?" It replies: $m_2 = 1$. You continue: $m_3 = 0$, $m_4 = 3$, and so on. You continue: $m_3 = 0$, $m_4 = 3$, and so on

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.

$$ m_n = \int_\mathbbR x^n , d\mu(x) $$

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