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The Generalized Chinese Restaurant Process (GCRP) describes a sequence of exchangeable random partitions of the numbers $\{1,\dots,n\}$. This process is related to the Ewens sampling model in Genetics and to Bayesian nonparametric methods…

Probability · Mathematics 2018-06-27 Alan Pereira , Roberto I. Oliveira , Rodrigo Ribeiro

We develop a heavy traffic diffusion limit theorem under nonstandard spatial scaling for the queue length process in a single server queue employing shortest remaining processing time (SRPT). For processing time distributions with unbounded…

Probability · Mathematics 2015-10-29 Amber L. Puha

The Pitman-Yor, or Chinese Restaurant Process, is a stochastic process that generates distributions following a power-law with exponents lower than two, as found in a numerous physical, biological, technological and social systems. We…

Disordered Systems and Neural Networks · Physics 2015-05-13 Bruno Bassetti , Mina Zarei , Marco Cosentino Lagomarsino , Ginestra Bianconi

Multisets are like sets, except that they can contain multiple copies of their elements. If there are $n_i$ copies of $i$, $1\leq i\leq t$, in multiset $M_t$, then there are $\binom{n_1+\cdots+n_t}{n_1,\ldots, n_t}$ possible permutations of…

Probability · Mathematics 2026-02-17 Dudley Stark

Dirichlet process mixture (DPM) models tend to produce many small clusters regardless of whether they are needed to accurately characterize the data - this is particularly true for large data sets. However, interpretability, parsimony, data…

Machine Learning · Computer Science 2018-02-16 Jun Lu , Meng Li , David Dunson

For a long time, the Dirichlet process has been the gold standard discrete random measure in Bayesian nonparametrics. The Pitman--Yor process provides a simple and mathematically tractable generalization, allowing for a very flexible…

Statistics Theory · Mathematics 2020-01-08 Caroline Lawless , Julyan Arbel

Bayesian nonparametric approaches, in particular the Pitman-Yor process and the associated two-parameter Chinese Restaurant process, have been successfully used in applications where the data exhibit a power-law behavior. Examples include…

Machine Learning · Statistics 2025-02-06 Fadhel Ayed , Juho Lee , François Caron

This paper studies the effect of an overdispersed arrival process on the performance of an infinite-server system. In our setup, a random environment is modeled by drawing an arrival rate $\Lambda$ from a given distribution every $\Delta$…

Probability · Mathematics 2016-02-02 Mariska Heemskerk , Johan van Leeuwaarden , Michel Mandjes

For $0<\alpha<1,$ and $\theta>-\alpha,$ let $(S^{-\alpha}_{\alpha,\theta+r})_{\{r\ge 0\}}$ denote an increasing(decreasing) sequence of variables forming a time inhomogeneous Markov chain whose marginal distributions are equivalent to…

Probability · Mathematics 2015-10-13 Lancelot F. James

This paper focuses on the problem of hierarchical non-overlapping clustering of a dataset. In such a clustering, each data item is associated with exactly one leaf node and each internal node is associated with all the data items stored in…

Machine Learning · Statistics 2021-05-26 Weipeng Huang , Nishma Laitonjam , Guangyuan Piao , Neil Hurley

Two families of stochastic interacting particle systems, the interacting Brownian motions and Bessel processes, are defined as extensions of Dyson's Brownian motion models and the eigenvalue processes of the Wishart and Laguerre processes…

Mathematical Physics · Physics 2014-06-09 Sergio Andraus

Dirichlet Process(DP) is a Bayesian non-parametric prior for infinite mixture modeling, where the number of mixture components grows with the number of data items. The Hierarchical Dirichlet Process (HDP), is an extension of DP for grouped…

Machine Learning · Statistics 2015-09-02 Lavanya Sita Tekumalla , Priyanka Agrawal , Indrajit Bhattacharya

An up-down chain is a Markov chain in which each transition is a two-step process that moves up to a larger object and then back down to an object of the original size. The first goal of this paper is to present a general framework for…

Probability · Mathematics 2025-12-24 Valentin Féray , Kelvin Rivera-Lopez

The Recurrent Chinese Restaurant Process (RCRP) is a powerful statistical method for modeling evolving clusters in large scale social media data. With the RCRP, one can allow both the number of clusters and the cluster parameters in a model…

Artificial Intelligence · Computer Science 2017-08-22 Wei Wei , Kennth Joseph , Kathleen Carley

The universal, scaled order parameter profiles $P_{\pm}(z/\xi)$ for critical adsorption of a fluid or fluid mixture onto a wall or interface, and for the extraordinary transition of the semi-infinite Ising model, are discussed…

Condensed Matter · Physics 2007-05-23 M. Smock , H. W. Diehl , D. P. Landau

Motivated by a down-up Markov chain on cladograms, David Aldous conjectured in 1999 that there exists a "diffusion on continuum trees" whose mass partitions at any finite number of branch points evolve as Wright-Fisher diffusions with some…

Probability · Mathematics 2023-05-30 Noah Forman , Soumik Pal , Douglas Rizzolo , Matthias Winkel

We establish a quenched functional central limit theorem for the total number of components of random partitions induced by Chinese restaurant process with parameters $(\alpha,\theta), \alpha\in(0,1), \theta>-\alpha$. With $P_j$ denoting…

Probability · Mathematics 2026-04-08 Yizao Wang

Let $\eta_t$ be a Poisson point process of intensity $t\geq 1$ on some state space $\Y$ and $f$ be a non-negative symmetric function on $\Y^k$ for some $k\geq 1$. Applying $f$ to all $k$-tuples of distinct points of $\eta_t$ generates a…

Probability · Mathematics 2012-12-11 Matthias Schulte , Christoph Thaele

We investigate a disordered variant of Pitman's Chinese restaurant process where tables carry i.i.d. weights. Incoming customers choose to sit at an occupied table with a probability proportional to the product of its occupancy and its…

Probability · Mathematics 2024-05-06 Jakob E. Björnberg , Cécile Mailler , Peter Mörters , Daniel Ueltschi

We study the persistence probability for processes with stationary increments. Our results apply to a number of examples: sums of stationary correlated random variables whose scaling limit is fractional Brownian motion, random walks in…

Probability · Mathematics 2019-05-01 Frank Aurzada , Nadine Guillotin-Plantard , Françoise Pène