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This paper investigates the location of the zeros of a sequence of polynomials generated by a rational function with a binomial-type denominator. We show that every member of a two-parameter family consisting of such generating functions…

Complex Variables · Mathematics 2016-06-28 Tamas Forgacs , Khang Tran

Probability distributions supported on the simplex enjoy a wide range of applications across statistics and machine learning. Recently, a novel family of such distributions has been discovered: the continuous categorical. This family enjoys…

Instead of dealing with cumbersome binomial identities, we prove Callan's result using generating functions.

Combinatorics · Mathematics 2007-05-23 Helmut Prodinger

This paper introduces the concept of a generating set for stochastic matrices -- a subset of matrices whose repeated composition generates the entire set. Understanding such generating sets requires specifying the "indivisible elements" and…

Rings and Algebras · Mathematics 2025-02-04 Frederik vom Ende , Fereshte Shahbeigi

We study the generating function of descent numbers for the permutations with descent pairs of prescribed parities, the distribution of which turns out to be a refinement of median Genocchi numbers. We prove the $\gamma$-positivity for the…

Combinatorics · Mathematics 2022-03-18 Sen-Peng Eu , Tung-Shan Fu , Hsin-Hao Lai , Yuan-Hsun Lo

We propose testing procedures for the hypothesis that a given set of discrete observations may be formulated as a particular time series of counts with a specific conditional law. The new test statistics incorporate the empirical…

Statistics Theory · Mathematics 2014-10-24 Šárka Hudecová , Marie Hušková , Simos G. Meintanis

The normal distribution is well-known for several results that it is the only to fulfil. The aim of the present paper is to show that many of these characterizations actually follow from the fact that the derivative of the log-density of…

Statistics Theory · Mathematics 2020-11-26 Christophe Ley

The manuscript studies configurations of non-overlapping non-bonding dominoes on finite rectangular boards of unit squares characterized by row and column number. The non-bonding dominoes are defined here by the requirement that any domino…

Combinatorics · Mathematics 2024-04-30 Richard J. Mathar

In this paper, we consider situations in which a given logical function is realized by a multithreshold threshold function. In such situations, constant functions can be easily obtained from multithreshold threshold functions, and…

Neural and Evolutionary Computing · Computer Science 2013-01-03 Yukihiro Kamada , Kiyonori Miyasaki

In this paper I describe two general methods of constructing a probability density function (pdf) of a Benford random variable, and show that the pdf of every Benford random variable may be constructed by these methods. These constructions…

General Mathematics · Mathematics 2020-08-27 Frank A. Benford

Preferences of individuals are distributions of elements generated by generalized functions. Models of economic decision-making derived from such distributions are consistent with results of physiological experiments, and explain any…

Neurons and Cognition · Quantitative Biology 2012-01-20 Leonid A. Shapiro

Generalization in generative modeling is defined as the ability to learn an underlying distribution from a finite dataset and produce novel samples, with evaluation largely driven by held-out performance and perceived sample quality. In…

Machine Learning · Computer Science 2026-03-05 Jerome Garnier-Brun , Luca Biggio , Davide Beltrame , Marc Mézard , Luca Saglietti

Probabilistic generative models of graphs are important tools that enable representation and sampling. Many recent works have created probabilistic models of graphs that are capable of representing not only entity interactions but also…

Machine Learning · Computer Science 2023-08-09 Pablo Robles-Granda , Katherine Tsai , Oluwasanmi Koyejo

Compositional generalization is the ability to generalize systematically to a new data distribution by combining known components. Although humans seem to have a great ability to generalize compositionally, state-of-the-art neural models…

Machine Learning · Computer Science 2021-06-22 Juyong Kim , Pradeep Ravikumar , Joshua Ainslie , Santiago Ontañón

Normalizing Flows are generative models which produce tractable distributions where both sampling and density evaluation can be efficient and exact. The goal of this survey article is to give a coherent and comprehensive review of the…

Machine Learning · Statistics 2020-06-09 Ivan Kobyzev , Simon J. D. Prince , Marcus A. Brubaker

We propose a new modeling approach that is a generalization of generative and discriminative models. The core idea is to use an implicit parameterization of a joint probability distribution by specifying only the conditional distributions.…

Machine Learning · Computer Science 2016-12-06 Dmitrij Schlesinger , Carsten Rother

A basic experiment in probability theory is drawing without replacement from an urn filled with multiple balls of different colours. Clearly, it is physically impossible to overdraw, that is, to draw more balls from the urn than it…

Probability · Mathematics 2023-12-21 Bart Jacobs , Dario Stein

The aim of this thesis is to determine classes of NP relations for which random generation and approximate counting problems admit an efficient solution. Since efficient rank implies efficient random generation, we first investigate some…

Computational Complexity · Computer Science 2010-12-15 Massimo Santini

It is demonstrated how an explicit expression of the (partial) sum of Tetranacci numbers can be found and proved using generating functions and the Hadamard product. We also provide a Binet-type formula for generalized Fibonacci numbers, by…

Number Theory · Mathematics 2020-01-22 Helmut Prodinger , Sarah J. Selkirk

We demonstrate that statistics for several types of set partitions are described by generating functions which appear in the theory of integrable equations.

Exactly Solvable and Integrable Systems · Physics 2017-05-30 V. E. Adler
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