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Consider a random permutation of $\{1, \ldots, \lfloor n^{t_2}\rfloor\}$ drawn according to the Ewens measure with parameter $t_1$ and let $K(n, t)$ denote the number of its cycles, where $t\equiv (t_1, t_2)\in\mathbb [0, 1]^2$. Next,…

Probability · Mathematics 2019-06-18 Helmut H. Pitters , Philip Weissmann

We study measures on random partitions, arising from condensing stochastic particle systems with stationary product distributions. We provide fairly general conditions on the stationary weights, which lead to Poisson-Dirichlet statistics of…

Probability · Mathematics 2023-03-06 Paul Chleboun , Simon Gabriel , Stefan Grosskinsky

A permutation $\sigma$ describing the relative orders of the first $n$ iterates of a point $x$ under a self-map $f$ of the interval $I=[0,1]$ is called an \emph{order pattern}. For fixed $f$ and $n$, measuring the points $x\in I$ (according…

Combinatorics · Mathematics 2010-03-30 Aaron Abrams , Eric Babson , Henry Landau , Zeph Landau , James Pommersheim

There has been great interest recently in applying nonparametric kernel mixtures in a hierarchical manner to model multiple related data samples jointly. In such settings several data features are commonly present: (i) the related samples…

Methodology · Statistics 2017-04-18 Jacopo Soriano , Li Ma

We study weak convergence of a sequence of point processes to a scale-invariant simple point process. For a deterministic sequence $(z_n)_{n\in\mathbb{N}}$ of positive real numbers increasing to infinity as $n \to \infty$ and a sequence…

Probability · Mathematics 2020-06-16 Chinmoy Bhattacharjee , Ilya Molchanov

We consider the behavior of extremal particles in $K$-symmetric exclusion on $\mathbb{Z}$ when the process starts from certain infinite-particle step configurations where there are no particles to the right of a maximal one. In such a…

Probability · Mathematics 2025-06-17 Michael Conroy , Adrián González Casanova , Sunder Sethuraman

Pitman(2003)(and subsequently Gnedin and Pitman (2006) showed that a large class of random partitions of the integers derived from a stable subordinator of index $\alpha\in(0,1)$ have infinite Gibbs (product) structure as a characterizing…

Probability · Mathematics 2018-07-31 Man-Wai Ho , Lancelot F. James , John W. Lau

This paper presents some new results on the conditional joint probability distributions of phase-type under the mixture of right-continuous Markov jump processes with absorption on the same finite state space $\mathbb{S}$ moving at…

Probability · Mathematics 2018-07-24 B. A. Surya

Habitat loss is one of the biggest threats facing plant species nowadays. We formulate a simple mathematical model of seed dispersal on reduced habitats to discuss survival of the species in relation to the habitat size and seeds production…

Probability · Mathematics 2023-05-24 Cristian F. Coletti , Nevena Marić , Pablo M. Rodriguez

We propose a flexible change-point model for inhomogeneous Poisson Processes, which arise naturally from next-generation DNA sequencing, and derive score and generalized likelihood statistics for shifts in intensity functions. We construct…

Applications · Statistics 2012-06-29 Jeremy J. Shen , Nancy R. Zhang

We introduce and study interval partition diffusions with Poisson--Dirichlet$(\alpha,\theta)$ stationary distribution for parameters $\alpha\in(0,1)$ and $\theta\ge 0$. This extends previous work on the cases $(\alpha,0)$ and…

Probability · Mathematics 2022-07-25 Noah Forman , Douglas Rizzolo , Quan Shi , Matthias Winkel

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world…

Methodology · Statistics 2016-12-28 David I. Inouye , Eunho Yang , Genevera I. Allen , Pradeep Ravikumar

We are interested in estimating the location of what we call "smooth change-point" from $n$ independent observations of an inhomogeneous Poisson process. The smooth change-point is a transition of the intensity function of the process from…

Statistics Theory · Mathematics 2021-02-17 A. Amiri , S Dachian

The Bayesian approach to inference stands out for naturally allowing borrowing information across heterogeneous populations, with different samples possibly sharing the same distribution. A popular Bayesian nonparametric model for…

Methodology · Statistics 2022-01-25 Antonio Lijoi , Igor Prünster , Giovanni Rebaudo

Suppose that a compound Poisson process is observed discretely in time and assume that its jump distribution is supported on the set of natural numbers. In this paper we propose a non-parametric Bayesian approach to estimate the intensity…

Statistics Theory · Mathematics 2020-05-21 Shota Gugushvili , Ester Mariucci , Frank van der Meulen

Variation in the evolutionary process across the sites of nucleotide sequence alignments is well established, and is an increasingly pervasive feature of datasets composed of gene regions sampled from multiple loci and/or different genomes.…

Populations and Evolution · Quantitative Biology 2014-09-04 Brian R. Moore , Jim McGuire , Fredrik Ronquist , John P. Huelsenbeck

Models of counts-of-counts data have been extensively used in the biological sciences, for example in cancer, population genetics, sampling theory and ecology. In this paper we explore properties of one model that is embedded into a…

Probability · Mathematics 2022-12-01 Poly H. da Silva , Arash Jamshidpey , Simon Tavaré

Original paper: We revisit the probability that any two consecutive events in a Poisson process N on [0,t] are separated by a time interval which is greater than s(<t) (a particular scan statistic probability), and the closely related…

Probability · Mathematics 2010-07-05 Shai Covo

The Ewens-Pitman model is a probability distribution for random partitions of the set $[n]=\{1,\ldots,n\}$, parameterized by $\alpha\in[0,1)$ and $\theta>-\alpha$, with $\alpha=0$ corresponding to the Ewens model in population genetics. The…

Probability · Mathematics 2025-03-11 Bernard Bercu , Stefano Favaro

Gibbs-type priors are widely used as key components in several Bayesian nonparametric models. By virtue of their flexibility and mathematical tractability, they turn out to be predominant priors in species sampling problems, clustering and…

Methodology · Statistics 2021-08-30 Federico Camerlenghi , Riccardo Corradin , Andrea Ongaro