Výsledky vyhledávání - "Paganin, Sally"

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Informed Random Partition Models with Temporal Dependence

Autor: Paganin, Sally, Page, Garritt L., Quintana, Fernando Andrés

Model-based clustering is a powerful tool that is often used to discover hidden structure in data by grouping observational units that exhibit similar response values. Recently, clustering methods have been developed that permit incorporating an ``in

Externí odkaz: http://arxiv.org/abs/2311.14502

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Computational methods for fast Bayesian model assessment via calibrated posterior p-values

Autor: Paganin, Sally, de Valpine, Perry

Posterior predictive p-values (ppps) have become popular tools for Bayesian model assessment, being general-purpose and easy to use. However, interpretation can be difficult because their distribution is not uniform under the hypothesis that the mode

Externí odkaz: http://arxiv.org/abs/2306.04866

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Computational strategies and estimation performance with Bayesian semiparametric Item Response Theory models

Autor: Paganin, Sally, Paciorek, Christopher J., Wehrhahn, Claudia, Rodriguez, Abel, Rabe-Hesketh, Sophia, de Valpine, Perry

Item response theory (IRT) models typically rely on a normality assumption for subject-specific latent traits, which is often unrealistic in practice. Semiparametric extensions based on Dirichlet process mixtures offer a more flexible representation

Externí odkaz: http://arxiv.org/abs/2101.11583

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Centered Partition Process: Informative Priors for Clustering

Autor: Paganin, Sally, Herring, Amy H., Olshan, Andrew F., Dunson, David B.

There is a very rich literature proposing Bayesian approaches for clustering starting with a prior probability distribution on partitions. Most approaches assume exchangeability, leading to simple representations in terms of Exchangeable Partition Pr

Externí odkaz: http://arxiv.org/abs/1901.10225

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Akademický článek

Computational Methods for Fast Bayesian Model Assessment via Calibrated Posterior p-values.

Autor: Paganin, Sally¹ (AUTHOR) paganin.1@osu.edu, de Valpine, Perry² (AUTHOR)

Publikováno v: Journal of Computational & Graphical Statistics. Jul2024, p1-12. 12p. 5 Illustrations.

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Bayesian modeling of networks in complex business intelligence problems

Autor: Durante, Daniele, Paganin, Sally, Scarpa, Bruno, Dunson, David B.

Publikováno v: Journal of the Royal Statistical Society: Series C (2017). 66, 555-580

Complex network data problems are increasingly common in many fields of application. Our motivation is drawn from strategic marketing studies monitoring customer choices of specific products, along with co-subscription networks encoding multiple purc

Externí odkaz: http://arxiv.org/abs/1510.00646

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