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Related papers: Gibbs fragmentation trees

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By developing data augmentation methods unique to the negative binomial (NB) distribution, we unite seemingly disjoint count and mixture models under the NB process framework. We develop fundamental properties of the models and derive…

Machine Learning · Statistics 2013-02-18 Mingyuan Zhou , Lawrence Carin

The present paper provides exact expressions for the probability distributions of linear functionals of the two-parameter Poisson--Dirichlet process $\operatorname {PD}(\alpha,\theta)$. We obtain distributional results yielding exact forms…

Probability · Mathematics 2009-09-29 Lancelot F. James , Antonio Lijoi , Igor Prünster

Binary trees are fundamental objects in models of evolutionary biology and population genetics. Here, we discuss some of their combinatorial and structural properties as they depend on the tree class considered. Furthermore, the process by…

Populations and Evolution · Quantitative Biology 2021-06-30 Thomas Wiehe

A di-sk tree is a rooted binary tree whose nodes are labeled by $\oplus$ or $\ominus$, and no node has the same label as its right child. The di-sk trees are in natural bijection with separable permutations. We construct a combinatorial…

Combinatorics · Mathematics 2021-09-15 Shishuo Fu , Zhicong Lin , Yaling Wang

Pairwise ordered tree alignment are combinatorial objects that appear in RNA secondary structure comparison. However, the usual representation of tree alignments as supertrees is ambiguous, i.e. two distinct supertrees may induce identical…

Quantitative Methods · Quantitative Biology 2016-03-08 Cedric Chauve , Julien Courtiel , Yann Ponty

Tree-decompositions and treewidth are of fundamental importance in structural and algorithmic graph theory. The "spread" of a tree-decomposition is the minimum integer $s$ such that every vertex lies in at most $s$ bags. A…

Combinatorics · Mathematics 2026-04-08 Marc Distel , Neel Kaul , Raj Kaul , David R. Wood

Double-descent refers to the unexpected drop in test loss of a learning algorithm beyond an interpolating threshold with over-parameterization, which is not predicted by information criteria in their classical forms due to the limitations…

Machine Learning · Computer Science 2023-11-15 Haobo Chen , Yuheng Bu , Gregory W. Wornell

Approximate Bayesian computation methods are useful for generative models with intractable likelihoods. These methods are however sensitive to the dimension of the parameter space, requiring exponentially increasing resources as this…

Computation · Statistics 2026-02-09 Grégoire Clarté , Christian P. Robert , Robin Ryder , Julien Stoehr

We consider the soft-core Widom-Rowlinson model for particles with spins and holes, on a Cayley tree of order $d$ (which has $d + 1$ nearest neighbours), depending on repulsion strength $\beta$ between particles of different signs and on an…

Probability · Mathematics 2023-02-14 Sebastian Bergmann , Sascha Kissel , Christof Kuelske

We introduce some natural families of distributions on rooted binary ranked plane trees with a view toward unifying ideas from various fields, including macroevolution, epidemiology, computational group theory, search algorithms and other…

Combinatorics · Mathematics 2017-08-22 Sean Cleary , Mareike Fischer , Robert C. Griffiths , Raazesh Sainudiin

Gibbs sampling, as a model learning method, is known to produce the most accurate results available in a variety of domains, and is a de facto standard in these domains. Yet, it is also well known that Gibbs random walks usually have…

Machine Learning · Statistics 2018-04-20 Mark Kozdoba , Shie Mannor

This article is a discussion of Zanella and Roberts' paper: Multilevel linear models, gibbs samplers and multigrid decompositions. We consider several extensions in which the multigrid decomposition would bring us interesting insights,…

Computation · Statistics 2021-12-17 Xiaodong Yang , Jun S. Liu

A generalization of the Poisson distribution based on the generalized Mittag-Leffler function $E_{\alpha, \beta}(\lambda)$ is proposed and the raw moments are calculated algebraically in terms of Bell polynomials. It is demonstrated, that…

Statistics Theory · Mathematics 2018-02-23 Richard Herrmann

This article considers a class of disordered mean-field combinatorial optimization problems. We focus on the Gibbs measure, where the inverse temperature does not vary with the size of the graph and the edge weights are sampled from a…

Probability · Mathematics 2024-02-13 Partha S. Dey , Grigory Terlov

In this paper we describe how MAP inference can be used to sample efficiently from Gibbs distributions. Specifically, we provide means for drawing either approximate or unbiased samples from Gibbs' distributions by introducing low…

Machine Learning · Computer Science 2013-10-01 Tamir Hazan , Subhransu Maji , Tommi Jaakkola

Within the framework of probability models for overdispersed count data, we propose the generalized fractional Poisson distribution (gfPd), which is a natural generalization of the fractional Poisson distribution (fPd), and the standard…

Probability · Mathematics 2021-01-12 Dexter Cahoy , Elvira Di Nardo , Federico Polito

The average node-to-node distance of scale-free graphs depends logarithmically on N, the number of nodes, while the probability distribution function (pdf) of the distances may take various forms. Here we analyze these by considering…

Statistical Mechanics · Physics 2009-11-07 Gabor Szabo , Mikko Alava , Janos Kertesz

Parametrizations of fragmentation functions (FFs) from $e^+$-$e^-$ and p-\=p collisions are combined with a parton spectrum model in a pQCD folding integral to produce minimum-bias {\em fragment distributions}. A model of in-medium FF…

High Energy Physics - Phenomenology · Physics 2009-10-08 Thomas A. Trainor

We prove a long-standing conjecture which characterises the Ewens-Pitman two-parameter family of exchangeable random partitions, plus a short list of limit and exceptional cases, by the following property: for each $n = 2,3, >...$, if one…

Probability · Mathematics 2009-11-20 Alexander Gnedin , Chris Haulk , Jim Pitman

The ability to compute the exact divergence between two high-dimensional distributions is useful in many applications but doing so naively is intractable. Computing the alpha-beta divergence -- a family of divergences that includes the…

Machine Learning · Computer Science 2023-10-17 Loong Kuan Lee , Geoffrey I. Webb , Daniel F. Schmidt , Nico Piatkowski