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Probability densities that are not uniquely determined by their moments are said to be "moment-indeterminate", or "M-indeterminate". Determining whether or not a density is M-indeterminate, or how to generate an M-indeterminate density, is…

Quantum Physics · Physics 2021-07-28 Rafael Sala Mayato , Patrick Loughlin , Leon Cohen

In this paper we consider the palindromes that can be formed by taking unordered sets of $n$ elements from an alphabet of $b$ letters. In particular, we seek to find the probability that given a random member of this space we are able to…

Combinatorics · Mathematics 2016-04-11 Alexander Burlton

Consider a density $f$ on $[0,1]$ that must be estimated from an i.i.d. sample $X_1,...,X_n$ drawn from $f$. In this note, we study binary-tree-based histogram estimates that use recursive splitting of intervals. If the decision to split an…

Statistics Theory · Mathematics 2025-04-24 Luc Devroye , Jad Hamdan

Estimating the density of a continuous random variable X has been studied extensively in statistics, in the setting where n independent observations of X are given a priori and one wishes to estimate the density from that. Popular methods…

Computation · Statistics 2021-09-09 Pierre L'Ecuyer , Florian Puchhammer

We define $\Psi$-autoreducible sets given an autoreduction procedure $\Psi$. Then, we show that for any $\Psi$, a measurable class of $\Psi$-autoreducible sets has measure zero. Using this, we show that classes of cototal, uniformly…

Logic · Mathematics 2024-02-14 Ang Li

We introduce a concept for random tilings which, comprising the conventional one, is also applicable to tiling ensembles without height representation. In particular, we focus on the random tiling entropy as a function of the tile…

Statistical Mechanics · Physics 2008-08-28 Christoph Richard , Moritz Hoeffe , Joachim Hermisson , Michael Baake

Intrinsic volumes, which generalize both Euler characteristic and Lebesgue volume, are important properties of $d$-dimensional sets. A random cubical complex is a union of unit cubes, each with vertices on a regular cubic lattice,…

Probability · Mathematics 2021-08-24 Michael Werman , Matthew L. Wright

For any infinite field k and any positive integer r, we show constructively that the map sending each polynomial P $\in$ k[x] to its r-th iterate is dominant in various inductive limit topologies on the space of all polynomials.

Algebraic Geometry · Mathematics 2025-11-27 Pascal Autissier , Jean-Philippe Furter , Egor Yasinsky

For a rational number $r>1$, a set $A$ of positive integers is called an $r$-multiple-free set if $A$ does not contain any solution of the equation $rx = y$. The extremal problem on estimating the maximum possible size of $r$-multiple-free…

Number Theory · Mathematics 2015-03-17 Sang June Lee

A coarse description of a subset A of omega is a subset D of omega such that the symmetric difference of A and D has asymptotic density 0. We study the extent to which noncomputable information can be effectively recovered from all coarse…

Logic · Mathematics 2015-05-08 Denis R. Hirschfeldt , Carl G. Jockusch , Rutger Kuyper , Paul E. Schupp

We generally study the density of eigenvalues in unitary ensembles of random matrices from the recurrence coefficients with regularly varying conditions for the orthogonal polynomials. First we calculate directly the moments of the density.…

Mathematical Physics · Physics 2008-10-31 Dang-Zheng Liu , Zheng-Dong Wang , Kui-Hua Yan

We consider the problem of estimating the density $\Pi$ of a determinantal process $N$ from the observation of $n$ independent copies of it. We use an aggregation procedure based on robust testing to build our estimator. We establish…

Statistics Theory · Mathematics 2013-03-15 Yannick Baraud

Kernel density estimation is a widely used nonparametric approach to estimate an unknown distribution. Recent work in Bayesian predictive inference has considered stochastic processes formed by specifying the predictive distribution for the…

Methodology · Statistics 2026-05-15 Torey Hilbert

Kernel density estimation is a popular method for estimating unseen probability distributions. However, the convergence of these classical estimators to the true density slows down in high dimensions. Moreover, they do not define meaningful…

Statistics Theory · Mathematics 2025-05-30 Jack Kendrick

Every K-trivial set is computable from an incomplete Martin-L\"of random set, i.e., a Martin-L\"of random set that does not compute 0'.

Many high dimensional integrals can be reduced to the problem of finding the relative measures of two sets. Often one set will be exponentially larger than the other, making it difficult to compare the sizes. A standard method of dealing…

Probability · Mathematics 2011-12-19 Mark Huber , Sarah Schott

High dimensional data can have a surprising property: pairs of data points may be easily separated from each other, or even from arbitrary subsets, with high probability using just simple linear classifiers. However, this is more of a rule…

Machine Learning · Computer Science 2023-11-15 Oliver J. Sutton , Qinghua Zhou , Alexander N. Gorban , Ivan Y. Tyukin

An iterative randomness extraction algorithm which generalized the Von Neumann's extraction algorithm is detailed, analyzed and implemented in standard C++. Given a sequence of independently and identically distributed biased Bernoulli…

Information Theory · Computer Science 2021-01-08 Claude Gravel

Let $p$ be a prime number and let $S=\{x^p+c_1,\dots,x^p+c_r\}$ be a finite set of unicritical polynomials for some $c_1,\dots,c_r\in\mathbb{Z}$. Moreover, assume that $S$ contains at least one irreducible polynomial over $\mathbb{Q}$. Then…

Number Theory · Mathematics 2023-08-29 Wade Hindes , Reiyah Jacobs , Benjamin Keller , Albert Kim , Peter Ye , Aaron Zhou

This paper explores a novel definition of Schnorr randomness for noncomputable measures. We say $x$ is uniformly Schnorr $\mu$-random if $t(\mu,x)<\infty$ for all lower semicomputable functions $t(\mu,x)$ such that $\mu\mapsto\int…

Logic · Mathematics 2017-08-08 Jason Rute