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We study an urn process containing red and blue balls and two different strategies to reinforce the urn. Namely, a generalized P\'olya-type strategy versus an i.i.d. one. At each step, one of the two reinforcement strategies is chosen by…

Probability · Mathematics 2019-03-14 Manuel González-Navarrete , Rodrigo Lambert

An urn contains black and red balls. Let $Z_n$ be the proportion of black balls at time $n$ and $0\leq L<U\leq 1$ random barriers. At each time $n$, a ball $b_n$ is drawn. If $b_n$ is black and $Z_{n-1}<U$, then $b_n$ is replaced together…

Probability · Mathematics 2015-08-27 Patrizia Berti , Irene Crimaldi , Luca Pratelli , Pietro Rigo

We consider an urn model, whose replacement matrix has all entries nonnegative and is balanced, that is, has constant row sums. We obtain the rates of the counts of balls corresponding to each color for the strong laws to hold. The analysis…

Probability · Mathematics 2017-09-05 Amites Dasgupta , Krishanu Maulik

There is much interest in providing probabilistic semantics for defaults but most approaches seem to suffer from one of two problems: either they require numbers, a problem defaults were intended to avoid, or they generate peculiar side…

Artificial Intelligence · Computer Science 2013-04-10 Eric Neufeld , David L Poole

Early investigation of P\'{o}lya urns considered drawing balls one at a time. In the last two decades, several authors considered multiple drawing in each step, but mostly for schemes on two colors. In this manuscript, we consider multiple…

Probability · Mathematics 2024-03-20 Joshua Sparks , Markus Kuba , Srinivasan Balaji , Hosam Mahmoud

A large amount of literature in social choice theory deals with quantifying the probability of certain election outcomes. One way of computing the probability of a specific voting situation under the Impartial Anonymous Culture assumption…

Optimization and Control · Mathematics 2014-06-23 Achill Schürmann

Probabilistic graphical modeling is a branch of machine learning that uses probability distributions to describe the world, make predictions, and support decision-making under uncertainty. Underlying this modeling framework is an elegant…

Machine Learning · Computer Science 2025-07-24 Jacqueline Maasch , Willie Neiswanger , Stefano Ermon , Volodymyr Kuleshov

This is a research endeavor in two parts. We study a class of balanced urn schemes on balls of two colours (say white and black). At each drawing, a sample of size $m\ge 1$ is drawn from the urn, and ball addition rules are applied. We…

Probability · Mathematics 2015-04-01 Markus Kuba , Hosam M. Mahmoud

The paper considers urn schemes in which several urns can be involved. Simplified formulas are proposed that allow direct calculation of probabilities without the use of elements combinatorics.

Probability · Mathematics 2019-08-21 Azam A. Imomov , Yorqin Khodjaev

A systematic study of the probability distribution of superimposed random codes is presented through the use of generating functions. Special attention is paid to the cases of either uniformly distributed but not necessarily independent or…

Databases · Computer Science 2008-06-19 Bernd Günther

Consider the following two-player game on the edges of $K_n$, the complete graph with $n$ vertices: Starting with an empty graph $G$ on the vertex set of $K_n$, in each round the first player chooses $b \in \mathbb{N}$ edges from $K_n$…

Combinatorics · Mathematics 2022-07-07 Rajko Nenadov

In computational social choice, the distortion of a voting rule quantifies the degree to which the rule overcomes limited preference information to select a socially desirable outcome. This concept has been investigated extensively, but…

Computer Science and Game Theory · Computer Science 2023-12-11 Yannai A. Gonczarowski , Gregory Kehne , Ariel D. Procaccia , Ben Schiffer , Shirley Zhang

Decision making under uncertainty is a key component of many AI settings, and in particular of voting scenarios where strategic agents are trying to reach a joint decision. The common approach to handle uncertainty is by maximizing expected…

Computer Science and Game Theory · Computer Science 2018-11-15 Omer Lev , Reshef Meir , Svetlana Obraztsova , Maria Polukarov

We present a multiple colour generalisation of the model of graph interacting urns studied by Benaim et. al., Random Struct. Alg., 46: 614-634, 2015. We show that for complete graphs and for a broad class of reinforcement functions…

Probability · Mathematics 2025-05-29 Benito Pires , Rafael A. Rosales

Given a finite connected graph G, place a bin at each vertex. Two bins are called a pair if they share an edge of G. At discrete times, a ball is added to each pair of bins. In a pair of bins, one of the bins gets the ball with probability…

Probability · Mathematics 2020-04-21 Michel Benaim , Itai Benjamini , Jun Chen , Yuri Lima

Statistical analysis of a graph often starts with embedding, the process of representing its nodes as points in space. How to choose the embedding dimension is a nuanced decision in practice, but in theory a notion of true dimension is…

Machine Learning · Statistics 2021-01-06 Patrick Rubin-Delanchy

A classical problem in statistics is estimating the expected coverage of a sample, which has had applications in gene expression, microbial ecology, optimization, and even numismatics. Here we consider a related extension of this problem to…

Statistics Theory · Mathematics 2013-02-11 Jerrad Hampton , Manuel E. Lladser

Random graphs have proven to be one of the most important and fruitful concepts in modern Combinatorics and Theoretical Computer Science. Besides being a fascinating study subject for their own sake, they serve as essential instruments in…

Combinatorics · Mathematics 2007-05-23 Michael Krivelevich , Benny Sudakov

In finite probability theory, events are subsets of the outcome set. Subsets can be represented by 1-dimensional column vectors. By extending the representation of events to two dimensional matrices, we can introduce "superposition events."…

Quantum Physics · Physics 2020-06-18 David Ellerman

The lottery ticket hypothesis conjectures the existence of sparse subnetworks of large randomly initialized deep neural networks that can be successfully trained in isolation. Recent work has experimentally observed that some of these…

Machine Learning · Computer Science 2022-03-17 Rebekka Burkholz , Nilanjana Laha , Rajarshi Mukherjee , Alkis Gotovos