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We take a unified approach to central limit theorems for a class of irreducible urn models with constant replacement matrix. Depending on the eigenvalue, we consider appropriate linear combinations of the number of balls of different…

Probability · Mathematics 2008-05-29 Gopal K. Basak , Amites Dasgupta

The negative binomial distribution (NBD) has been theorized to express a scale-invariant property of many-body systems and has been consistently shown to outperform other statistical models in both describing the multiplicity of…

High Energy Physics - Phenomenology · Physics 2023-10-09 S. V. Tezlaf

Following a paper in which the fundamental aspects of probabilistic inference were introduced by means of a toy experiment, details of the analysis of simulated long sequences of extractions are shown here. In fact, the striking performance…

History and Overview · Mathematics 2017-01-09 Giulio D'Agostini

Many probabilistic programming languages allow programs to be run under constraints in order to carry out Bayesian inference. Running programs under constraints could enable other uses such as rare event simulation and probabilistic…

Programming Languages · Computer Science 2015-01-19 Neil Toronto , Jay McCarthy , David Van Horn

Measurement theory is the cornerstone of science, but no equivalent theory underpins the huge volumes of non-numerical data now being generated. In this study, we show that replacing numbers with alternative mathematical models, such as…

Other Computer Science · Computer Science 2020-12-23 David G. Green , Kerri Morgan , Marc Cheong

Bigraphs are a universal computational modelling formalism for the spatial and temporal evolution of a system in which entities can be added and removed. We extend bigraphs to probablistic bigraphs, and then again to action bigraphs, which…

Logic in Computer Science · Computer Science 2022-06-28 Blair Archibald , Muffy Calder , Michele Sevegnani

This paper concerns two-player alternating play combinatorial games (Conway 1976) in the normal-play convention, i.e. last move wins. Specifically, we study impartial vector subtraction games on tuples of nonnegative integers (Golomb 1966),…

Combinatorics · Mathematics 2024-01-17 Urban Larsson , Indrajit Saha , Makoto Yokoo

In this paper we leverage on probability over Riemannian manifolds to rethink the interpretation of priors and posteriors in Bayesian inference. The main mindshift is to move away from the idea that "a prior distribution establishes a…

Statistics Theory · Mathematics 2021-06-03 Jesus Cerquides

Dimension reduction is the process of embedding high-dimensional data into a lower dimensional space to facilitate its analysis. In the Euclidean setting, one fundamental technique for dimension reduction is to apply a random linear map to…

Probability · Mathematics 2017-09-19 Samet Oymak , Joel A. Tropp

Deterministic fabrication of random metamaterials requires filling of a space with randomly oriented and randomly positioned chords with an on-average homogenous density and orientation, which is a nontrivial task. We describe a method to…

The aim of this paper is to establish a theory of random variables on domains. Domain theory is a fundamental component of theoretical computer science, providing mathematical models of computational processes. Random variables are the…

Logic in Computer Science · Computer Science 2016-08-30 Michael W. Mislove

Minimal surfaces and domain walls play important roles in various contexts of spacetime physics as well as material science. In this paper, we first review the Bernstein conjecture, which asserts that a plane is the only globally well…

High Energy Physics - Theory · Physics 2009-09-28 Gary W. Gibbons , Kei-ichi Maeda , Umpei Miyamoto

We study several exactly solvable Polya-Eggenberger urn models with a \emph{diminishing} character, namely, balls of a specified color, say $x$ are completely drawn after a finite number of draws. The main quantity of interest here is the…

Combinatorics · Mathematics 2022-12-13 Hsien-Kuei Hwang , Markus Kuba , Alois Panholzer

We introduce a multi-colour multi-urn generalisation of the Bernoulli-Laplace urn model, consisting of $d$ urns, $m$ colours, and $dmn$ balls, with $dn$ balls of each colour and $mn$ balls in each urn. At each step, one ball is drawn…

Probability · Mathematics 2025-11-14 Ritesh Goenka , Jonathan Hermon , Dominik Schmid

Chance constraints yield non-convex feasible regions in general. In particular, when the uncertain parameters are modeled by a Wasserstein ball, arXiv:1806.07418 and arXiv:1809.00210 showed that the distributionally robust (pessimistic)…

Optimization and Control · Mathematics 2025-03-14 Haoming Shen , Ruiwei Jiang

We extend the theory of probability graphons, continuum representations of edge-decorated graphs arising in graph limits theory, to the 'right convergence' point of view. First of all, we generalise the notions of overlay functionals and…

Probability · Mathematics 2024-07-09 Giulio Zucal

In this paper we discuss a class of double sums involving ratios of binomial coefficients. The sums are of the form \[ \sum_{j=0}^{n} \sum_{i=0}^j \frac{\binom{f_1(n)}{i}}{\binom{f_2(n)}{j}}\,c^{i-j}, \] where $f_1, f_2$ are functions of…

Combinatorics · Mathematics 2018-09-27 David Stenlund , James G. Wan

Hindman's celebrated Finite Sums Theorem, and its high-dimensional version due to Milliken and Taylor, are extended from covers of countable sets to covers of arbitrary topological spaces with Menger's classic covering property. The methods…

General Topology · Mathematics 2017-11-09 Boaz Tsaban

Consider an urn model whose replacement matrix is triangular, has all entries nonnegative and the row sums are all equal to one. We obtain the strong laws for the counts of balls corresponding to each color. The scalings for these laws…

Probability · Mathematics 2010-09-27 Arup Bose , Amites Dasgupta , Krishanu Maulik

Probabilistic graphical models combine the graph theory and probability theory to give a multivariate statistical modeling. They provide a unified description of uncertainty using probability and complexity using the graphical model.…

Machine Learning · Statistics 2011-11-30 Yang Zhou