קולוקוויום Ical Atom רשימת תפוצה

The central limit theorem (CLT) and related results for stationary weakly dependent sequences of random variables have been extensively studied in the past century, starting from a pioneering work of Berenstien (1927). However, in many physical phenomena there are external forces, measurement errors and unknown variables (e.g. storms, the observer effect, the uncertainty principle etc.). This means that the local laws of physics depend on time, and it leads us to studying non-stationary sequences.

The asymptotic behaviour of non-stationary sequences have been studied extensively in the past decades, but it is still developing compared with the theory of stationary processes. In this talk we will focus on inhomogeneous Markov chains. For sufficiently well contracting Markov chains the CLT was first proven by Dobrushin (1956). Since then many results were proven for stationary chains. In 2021 Dolgopyat and Sarig proved local central limit theorems (LCLT) for inhomogeneous Markov chains. In 2022 Dolgopyat and H proved optimal CLT rates in Dobrusin‘s CLT. These results closed a big gap in literature concerning the non-stationary case.

An open problem raised by Dolgopyat and Sarig in their 2021 book concerns limit theorems for Markov shifts, that is when the underlying sequence of functions that forms the partial sums depend on the entire path of the chain. Two circumstances where such dependence arises are products of random matrices and random iterated functions, and there are many other instances when the functionals depend on the entire path.

In this talk we will present our solution to the above problem. More precisely, we prove CLT, optimal CLT rates and LCLT for a wide class of sufficiently well mixing Markov chains and functionals with infinite memory. Even though the inhomogeneous case is more complicated, our results seem to be new already for stationary chains.

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We consider quasi-orthogonal polynomial families - those that are orthogonal with respect to a non-degenerate bilinear form defined by a linear functional - in which the ratio of successive coefficients is given by a rational function f(n,k) which is polynomial in n. Here, n is the index of the polynomial, and k of the coefficient. We show that, up to rescaling and renormalization, there are only five such families.

More generally, we define an auxiliary basis for the space of polynomials, called Newtonian bases, and consider coefficients with respect to this basis rather than the standard monomial basis. We call the polynomial families that satisfy the rationality conditions on ratio of successive coefficients with respect to this basis HG-families. We show that, up to rescaling, shift, and renormalization, there are only 10 quasi-orthogonal HG-families. Each family arises as a specialization of some hypergeometric series. I will define this notion in the talk. Eight of the 10 families are classical very useful polynomial families, and we view our theorem as a classification result in the theory of special functions.

We also consider the more general rational HG-families, i.e. quasi-orthogonal families in which the ratio f(n,k) of successive coefficients is allowed to be rational in n as well. I will formulate the two main theorems, one on quasi-orthogonal HG-families and one on rational quasi-orthogonal HG-families, as well as the main ideas of the proofs. They are of algebraic nature.

This is a joint work with Joseph Bernstein and Siddhartha Sahi.