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Gaussian processes (GPs) provide a probabilistic nonparametric representation of functions in regression, classification, and other problems. Unfortunately, exact learning with GPs is intractable for large datasets. A variety of approximate…

Machine Learning · Computer Science 2012-03-19 Yuan , Qi , Ahmed H. Abdel-Gawad , Thomas P. Minka

We analyze a class of models for unequal crossover (UC) of sequences containing sections with repeated units that may differ in length. In these, the probability of an `imperfect' alignment, in which the shorter sequence has d units without…

Dynamical Systems · Mathematics 2007-05-23 Oliver Redner , Michael Baake

The $LS$-sequences are a parametric family of sequences of points in the unit interval. They were introduced by Carbone, who also proved that under an appropriate choice of the parameters $L$ and $S$, such sequences are low-discrepancy. The…

Number Theory · Mathematics 2015-03-26 Maria Rita Iacò , Volker Ziegler

Using sequences of finite length with positive integer elements and the inversion statistic on such sequences, a collection of binomial and multinomial identities are extended to their $q$-analog form via combinatorial proofs. Using the…

Combinatorics · Mathematics 2020-05-18 Adrian Avalos , Mark Bly

We propose a similarity measure for sparsely sampled time course data in the form of a log-likelihood ratio of Gaussian processes (GP). The proposed GP similarity is similar to a Bayes factor and provides enhanced robustness to noise in…

Machine Learning · Computer Science 2021-02-25 Zijing Liu , Mauricio Barahona

For every connected graph $G$ and surface $S$, we consider the well-known string of inequalities $\delta_S(G) \leq \mu_S(G) \leq \nu_S(G)$, where $\mu$ and $\nu$ denote skewness and crossing number and $\delta$ is the Euler-formula lower…

Combinatorics · Mathematics 2025-01-07 Paul C. Kainen

This paper is a modified chapter of the author's Ph.D. thesis. We introduce the notions of sequentially approximated types and sequentially approximated Keisler measures. As the names imply, these are types which can be approximated by a…

Logic · Mathematics 2021-12-13 Kyle Gannon

In this paper, based on Serre's $p$-adic family of Eisenstein series, we prove a general family of congruences for Eisenstein series $G_k$ in the form $$ \sum_{i=1}^n g_i(p)G_{f_i(p)}\equiv g_0(p)\mod p^N, $$ where…

Number Theory · Mathematics 2021-06-22 Su Hu , Min-Soo Kim , Min Sha

For a complex quasi-projective manifold with a finite group action, we define higher order generalized Euler characteristics with values in the Grothendieck ring of complex quasi-projective varieties extended by the rational powers of the…

Algebraic Geometry · Mathematics 2013-03-25 S. M. Gusein-Zade , I. Luengo , A. Melle-Hernández

The subgroup pattern of a finite group $G$ is the table of marks of $G$ together with a list of representatives of the conjugacy classes of subgroups of $G$. In this article we describe a collection of sequences realized by the subgroup…

Group Theory · Mathematics 2013-06-14 Liam Naughton , Goetz Pfeiffer

We reformulate the symmetries of Gurses [Phys. Rev. Lett. 70, 367 (1993)] in a more abstract, more geometrical manner. The type (b) transformation of \gurses\ is related to a diffeomorphism of the differentiable manifold onto itself. The…

General Relativity and Quantum Cosmology · Physics 2009-10-22 Frederick J. Ernst , Isidore Hauser

A (d-parameter) basic nilsequence is a sequence of the form \psi(n)=f(a^{n}x), n \in Z^{d}, where x is a point of a compact nilmanifold X, a is a translation on X, and f is a continuous function on X; a nilsequence is a uniform limit of…

Dynamical Systems · Mathematics 2019-11-06 Alexander Leibman

Universal outlier hypothesis testing is studied in a sequential setting. Multiple observation sequences are collected, a small subset of which are outliers. A sequence is considered an outlier if the observations in that sequence are…

Statistics Theory · Mathematics 2014-11-27 Yun Li , Sirin Nitinawarat , Venugopal V. Veeravalli

This article introduces the Generalized Fourier Series (GFS), a novel spectral method that extends the clas- sical Fourier series to non-periodic functions. GFS addresses key challenges such as the Gibbs phenomenon and poor convergence in…

Numerical Analysis · Mathematics 2025-10-17 Narsimha Reddy Rapakaa , Mohamed Kamel Riahi

The vast quantity of information brought by big data as well as the evolving computer hardware encourages success stories in the machine learning community. In the meanwhile, it poses challenges for the Gaussian process (GP) regression, a…

Machine Learning · Statistics 2019-04-10 Haitao Liu , Yew-Soon Ong , Xiaobo Shen , Jianfei Cai

Based on Euclid's algorithm, we find a kind of special sequences which play an interesting role in the study of primes. We call them W Sequences. They not only ties up the distribution of primes in short interval but also enables us to give…

General Mathematics · Mathematics 2009-09-15 Shaohua Zhang

We introduce a new class of nonstationary kernels, which we derive as covariance functions of a novel family of stochastic processes we refer to as string Gaussian processes (string GPs). We construct string GPs to allow for multiple types…

Machine Learning · Statistics 2015-06-09 Yves-Laurent Kom Samo , Stephen Roberts

Gaussian Processes (GPs) can be used as flexible, non-parametric function priors. Inspired by the growing body of work on Normalizing Flows, we enlarge this class of priors through a parametric invertible transformation that can be made…

Machine Learning · Computer Science 2021-02-26 Juan Maroñas , Oliver Hamelijnck , Jeremias Knoblauch , Theodoros Damoulas

Generating functions for the number of commuting m-tuples in the symmetric groups are obtained. We define a natural sequence of ``orbifold Euler characteristics'' for a finite group G acting on a manifold X. Our definition generalizes the…

Combinatorics · Mathematics 2007-05-23 Jim Bryan , Jason Fulman

A sequence of graphs is FO-convergent if the probability of satisfaction of every first-order formula converges. A graph modeling is a graph, whose domain is a standard probability space, with the property that every definable set is Borel.…

Combinatorics · Mathematics 2026-04-15 J. Nesetril , P. Ossona de Mendez