Hierarchical normalized completely random measures for robust graphical modeling

DOI10.1214/19-BA1153zbMath1435.62121OpenAlexW2927563806WikidataQ95294517 ScholiaQ95294517MaRDI QIDQ2290716

Katherine Shoemaker, Christine Peterson, Andrea Cremaschi, Raffaele Argiento, Marina Vannucci

Publication date: 29 January 2020

Published in: Bayesian Analysis (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ba/1553738429

zbMATH Keywords

Bayesian nonparametricsgraphical modelshierarchical modelsnormalized completely random measures\(t\)-distributionradiomics data

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05) Random measures (60G57)

Related Items (8)

Bayesian graphical models for modern biological applications ⋮ Rejoinder to the discussion of ``Bayesian graphical models for modern biological applications ⋮ Gaussian graphical modeling for spectrometric data analysis ⋮ Bayesian sparse seemingly unrelated regressions model with variable selection and covariance estimation via the horseshoe+ ⋮ Perfect sampling of the posterior in the hierarchical Pitman-Yor process ⋮ Hierarchical species sampling models ⋮ Hierarchical normalized completely random measures for robust graphical modeling ⋮ Dynamic and robust Bayesian graphical models

Uses Software

• glasso
• BNPdensity
• HdBCS

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