Evidence map›Paper›PMID 39691937›Full record

ArticleJournal of machine learning research : JMLR2024

Graphical Dirichlet Process for Clustering Non-Exchangeable Grouped Data.

Arhit Chakrabarti, Yang Ni, Ellen Ruth A Morris, Michael L Salinas, Robert S Chapkin, Bani K Mallick

Abstract read

In one paragraph

Article in Journal of machine learning research : JMLR, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Bayesian Transfer Learning.Statistical science : a review journal of the Institute of Mathematical Statistics · 2025
Article

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Arhit ChakrabartiDepartment of Statistics, Texas A&M University, College Station, TX 77843-3143, USA.

Yang NiDepartment of Statistics, CPRIT Single Cell Data Science Core, Texas A&M University, College Station, TX 77843-3143, USA.

Ellen Ruth A MorrisDepartment of Nutrition, Program in Integrative Nutrition & Complex Diseases, Current address: Texas A&M Veterinary Medical Diagnostic Laboratory, Texas A&M University College Station, TX 77843-4471, USA.

Michael L SalinasDepartment of Nutrition, Program in Integrative Nutrition & Complex Diseases, CPRIT Single Cell Data Science Core, Texas A&M University, College Station, TX 77843-2253, USA.

Robert S ChapkinDepartment of Nutrition, Program in Integrative Nutrition & Complex Diseases, CPRIT Single Cell Data Science Core, Texas A&M University, College Station, TX 77843-2253, USA.

Bani K MallickDepartment of Statistics, Texas A&M University, College Station, TX 77843-3143, USA.

Funding

Targeting plasma membrane spatial dynamics to suppress aberrant Wnt signalingR01CA244359 · NCI · TEXAS A&M AGRILIFE RESEARCH · PI CHAPKIN, ROBERT STEPHEN, KARPAC, JASON S · 2020 to 2024

$2.7M

Bayesian Differential Causal Network and Clustering Methods for Single-Cell Data (R01GM148974)R01GM148974 · NIGMS · UNIVERSITY OF TEXAS AT AUSTIN · PI NI, YANG · 2022 to 2025

$1.2M

NCI NIH HHS R01 CA244359NIGMS NIH HHS R01 GM148974

6 · The paper itself

Abstract

We consider the problem of clustering grouped data with possibly non-exchangeable groups whose dependencies can be characterized by a known directed acyclic graph. To allow the sharing of clusters among the non-exchangeable groups, we propose a Bayesian nonparametric approach, termed graphical Dirichlet process, that jointly models the dependent group-specific random measures by assuming each random measure to be distributed as a Dirichlet process whose concentration parameter and base probability measure depend on those of its parent groups. The resulting joint stochastic process respects the Markov property of the directed acyclic graph that links the groups. We characterize the graphical Dirichlet process using a novel hypergraph representation as well as the stick-breaking representation, the restaurant-type representation, and the representation as a limit of a finite mixture model. We develop an efficient posterior inference algorithm and illustrate our model with simulations and a real grouped single-cell data set.

Indexed as

Bayesian nonparametricsclusteringdirected acyclic graphfamily-owned restaurant processnon-exchangeable groups

Identifiers

PMID39691937

PMCPMC11650374

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.