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Mix-cumulants of conserved charge distributions are sensitive observables for probing properties of the QCD medium and phase transition in heavy-ion collisions. To perform precise measurements, efficiency correction is one of the most…

Data Analysis, Statistics and Probability · Physics 2021-08-03 Arghya Chatterjee , Toshihiro Nonaka , ShinIchi Esumi , Xiaofeng Luo

This manuscript investigates unconditional and conditional-on-stopping maximum likelihood estimators (MLEs), information measures and information loss associated with conditioning in group sequential designs (GSDs). The possibility of early…

Methodology · Statistics 2019-08-06 Sergey Tarima , Nancy Flournoy

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

With the advent of ubiquitous monitoring and measurement protocols, studies have started to focus more and more on complex, multivariate and heterogeneous datasets. In such studies, multivariate response variables are drawn from a…

Methodology · Statistics 2023-03-03 Saverio Ranciati , Veronica Vinciotti , Ernst C. Wit , Giuliano Galimberti

This paper introduces constrained mixtures for continuous distributions, characterized by a mixture of distributions where each distribution has a shape similar to the base distribution and disjoint domains. This new concept is used to…

Machine Learning · Statistics 2015-03-29 Conrado S. Miranda , Fernando J. Von Zuben

Circular data are data measured in angles and occur in a variety of scientific disciplines. Bayesian methods promise to allow for flexible analysis of circular data. Three existing MCMC methods (Gibbs, Metropolis-Hastings, and Rejection)…

Computation · Statistics 2015-05-12 Kees Tim Mulder , Irene Klugkist

Constrained clustering has gained significant attention in the field of machine learning as it can leverage prior information on a growing amount of only partially labeled data. Following recent advances in deep generative models, we…

Machine Learning · Computer Science 2022-02-02 Laura Manduchi , Kieran Chin-Cheong , Holger Michel , Sven Wellmann , Julia E. Vogt

It is suggested that the study of multiplicity difference correlators between two well-separated bins in high-energy heavy-ion collisions can be used as a means to detect evidence of a quark-hadron phase transition. Analytical expressions…

High Energy Physics - Phenomenology · Physics 2008-11-26 C. B. Yang , X. Cai

Globular clusters (GCs) in the Milky Way have characteristic velocity dispersions that are consistent with the predictions of Newtonian gravity, and may be at odds with Modified Newtonian Dynamics (MOND). We discuss a modified gravity (MOG)…

Astrophysics · Physics 2008-06-14 J. W. Moffat , V. T. Toth

Convolution is a broadly useful operation with applications including signal processing, machine learning, probability, optics, polynomial multiplication, and efficient parsing. Usually, however, this operation is understood and implemented…

Programming Languages · Computer Science 2019-03-27 Conal Elliott

Multivariate hypergeometric distribution arises frequently in elementary statistics and probability courses, for simultaneously studying the occurence law of specified events, when sampling without replacement from a finite population with…

Statistics Theory · Mathematics 2021-01-05 X. G. Duan

A generalized so called magnetically controlled ballistic rain-like deposition (MBD) model of granular piles has been numerically investigated in 2D. The grains are taken to be elongated disks whence characterized by a two-state scalar…

Statistical Mechanics · Physics 2016-08-31 K. Trojan , M. Ausloos , R. Cloots

We present the CBM physics performance study for measurements of the higher order cumulants of the net-proton multiplicity distributions. These observables are proxy for net-baryon fluctuations and are commonly used to study the phase…

Nuclear Experiment · Physics 2021-02-03 Subhasis Samanta

Universal compression algorithms have been studied in the past for sequential change detection, where they have been used to estimate the post-change distribution in the modified version of the Cumulative Sum (CUSUM) Test. In this paper, we…

Information Theory · Computer Science 2021-12-15 Vikrant Malik , R. K. Bansal

A composite likelihood is an inference function derived by multiplying a set of likelihood components. This approach provides a flexible framework for drawing inference when the likelihood function of a statistical model is computationally…

Methodology · Statistics 2024-12-10 Giuseppe Alfonzetti , Ruggero Bellio , Yunxiao Chen , Irini Moustaki

In actuarial practice, the usual independence assumptions for the collective risk model are often violated, implying a growing need for considering more general models that incorporate dependence. To this purpose, the present paper studies…

Probability · Mathematics 2024-06-26 Spyridon M. Tzaninis , Apostolos Bozikas

We examine capabilities of the modified perturbation theory (MPT) for description of the processes with productions and decays of fundamental unstable particles. We calculate total cross-section for $e^{+} e^{-} \to \gamma,Z \to W^{+} W^{-}…

High Energy Physics - Phenomenology · Physics 2021-01-25 M. L. Nekrasov

We present a full Monte Carlo simulation of the multiplicity and eccentricity distributions in U+U collisions at sqrt(s) = 200 A GeV. While unavoidable trigger inefficiencies in selecting full-overlap U+U collisions cause significant…

Nuclear Theory · Physics 2009-10-09 Anthony J. Kuhlman , Ulrich W. Heinz

The particle Gibbs sampler is a Markov chain Monte Carlo (MCMC) algorithm to sample from the full posterior distribution of a state-space model. It does so by executing Gibbs sampling steps on an extended target distribution defined on the…

Computation · Statistics 2015-07-29 Nicolas Chopin , Sumeetpal S. Singh

We introduce and study a multiparameter version of the generalized counting process (GCP), where there is a possibility of finitely many arrivals simultaneously. We call it the multiparameter GCP. In a particular case, it is uniquely…

Probability · Mathematics 2025-10-06 Manisha Dhillon , Kuldeep Kumar Kataria