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Inspired by Stein's lemma, we derive two expressions for the joint moments of elliptical distributions. We use two different methods to derive $E[X_{1}^{2}f(\mathbf{X})]$ for any measurable function $f$ satisfying some regularity…

Statistics Theory · Mathematics 2020-08-04 Baishuai Zuo , Chuancun Yin , Narayanaswamy Balakrishnan

Representing token embeddings as probability distributions over learned manifolds allows for more flexible contextual inference, reducing representational rigidity while enhancing semantic granularity. Comparative evaluations demonstrate…

Computation and Language · Computer Science 2025-04-25 Christopher Nightingale , Dominic Lavington , Jonathan Thistlethwaite , Sebastian Penhaligon , Thomas Belinski , David Boldo

In this article we provide an experimental algorithm that in many cases gives us an upper bound of the global infimum of a real polynomial on $\R^{n}$. It is very well known that to find the global infimum of a real polynomial on $\R^{n}$,…

Optimization and Control · Mathematics 2018-09-25 María López Quijorna

We use tropicalization to study the duals to cones of nonnegative polynomials and sums of squares on a semialgebraic set $S$. The truncated cones of moments of measures supported on the set $S$ is dual to nonnegative polynomials on $S$,…

Algebraic Geometry · Mathematics 2025-09-03 Grigoriy Blekherman , Felipe Rincón , Rainer Sinn , Cynthia Vinzant , Josephine Yu

Non-commutative polynomial optimization is a powerful technique with numerous applications in quantum nonlocality, quantum key distribution, causal inference, many-body physics, amongst others. The standard approach is to reduce such…

Quantum Physics · Physics 2024-06-25 Andrew J. P. Garner , Mateus Araújo

We establish the asymptotics of the joint moments of the characteristic polynomial of a random unitary matrix and its derivative for general real values of the exponents, proving a conjecture made by Hughes in 2001. Moreover, we give a…

Probability · Mathematics 2021-11-08 Theodoros Assiotis , Jonathan P. Keating , Jon Warren

We consider a dominance order on positive vectors induced by the elementary symmetric polynomials. Under this dominance order we provide conditions that yield simple proofs of several monotonicity questions. Notably, our approach yields a…

Classical Analysis and ODEs · Mathematics 2017-06-26 Suvrit Sra

We introduce novel polyhedral approximation hierarchies for the cone of nonnegative forms on the unit sphere in $\mathbb{R}^n$ and for its (dual) cone of moments. We prove computable quantitative bounds on the speed of convergence of such…

Optimization and Control · Mathematics 2025-12-29 Sergio Cristancho , Mauricio Velasco

An asymptotic expansion for inverse moments of positive binomial and Poisson distributions is derived. The expansion coefficients of the asymptotic series are given by the positive central moments of the distribution. Compared to previous…

Statistics Theory · Mathematics 2009-01-16 Marko Znidaric

We use Stein characterizations to obtain new moment-type estimators for the parameters of three classical spherical distributions (namely the Fisher-Bingham, the von Mises-Fisher, and the Watson distributions) in the i.i.d. case. This leads…

Statistics Theory · Mathematics 2024-07-05 Adrian Fischer , Robert E. Gaunt , Yvik Swan

In this short note, explicit formulas are developed for the central and noncentral moments of the multivariate hypergeometric distribution. A numerical implementation is provided in Mathematica for fast evaluations. This work complements…

Probability · Mathematics 2024-04-16 Frédéric Ouimet

The randomized quantum marginal problem asks about the joint distribution of the partial traces ("marginals") of a uniform random Hermitian operator with fixed spectrum acting on a space of tensors. We introduce a new approach to this…

Mathematical Physics · Physics 2023-04-18 Sho Matsumoto , Colin McSwiggen

Moment restrictions and their conditional counterparts emerge in many areas of machine learning and statistics ranging from causal inference to reinforcement learning. Estimators for these tasks, generally called methods of moments, include…

Machine Learning · Computer Science 2023-06-14 Heiner Kremer , Yassine Nemmour , Bernhard Schölkopf , Jia-Jie Zhu

The uniform probability measure on a convex polytope induces piecewise polynomial densities on its projections. For a fixed combinatorial type of simplicial polytopes, the moments of these measures are rational functions in the vertex…

Algebraic Geometry · Mathematics 2020-07-08 Kathlén Kohn , Boris Shapiro , Bernd Sturmfels

Moments of the characteristic polynomial of a random matrix taken from any of the three ensembles, orthogonal, unitary or symplectic, are given either as a determinant or a pfaffian or as a sum of determinants. For gaussian ensembles…

Statistical Mechanics · Physics 2007-05-23 M. L. Mehta , J. -M. Normand

We describe efficient algorithms to search for cases in which binomial coefficients are equal or almost equal, give a conjecturally complete list of all cases where two binomial coefficients differ by 1, and give some identities for…

Number Theory · Mathematics 2017-10-16 Aart Blokhuis , Andries Brouwer , Benne de Weger

Let $f$ be a cuspidal eigenform of weight $k$ on $\SL_2(\BZ)$ and let $\lambda_{\Sym^d f}(n)$ be the normalized Fourier coefficients of its $d$-th symmetric power lift. This paper establishes asymptotic formulas for the moments $\sum_{n\leq…

Number Theory · Mathematics 2026-02-03 Jiong Yang , Zhishan Yang

We consider the distribution of the binomial probability mass function (pmf) among arithmetic progressions and obtain an average-type theorem. As applications, we consider the possible visits to a kind of sieved sets of integers or lattice…

Number Theory · Mathematics 2023-07-07 Jun Hong , Xiaosheng Wu , Shixin Zhu

We introduce a theory of probability in $\lambda$-rings designed to efficiently describe random variables valued in multisets of complex numbers, varieties over a field, or other similar enriched settings. A key role is played by the…

Number Theory · Mathematics 2025-06-10 Sean Howe

Bayesian change-point detection, together with latent variable models, allows to perform segmentation over high-dimensional time-series. We assume that change-points lie on a lower-dimensional manifold where we aim to infer subsets of…

Machine Learning · Statistics 2020-11-04 Lorena Romero-Medrano , Pablo Moreno-Muñoz , Antonio Artés-Rodríguez