ArticleBayesian analysis2019
Hierarchical Normalized Completely Random Measures for Robust Graphical Modeling.
Article in Bayesian analysis, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Bayesian semi-parametric inference for joint modelling of childhood growth and appetite phenotypes.BMC medical research methodology · 2026Article
- Covariate-Assisted Bayesian Graph Learning for Heterogeneous Data.Journal of the American Statistical Association · 2024Article
- Perfect Sampling of the Posterior in the Hierarchical Pitman-Yor Process.Bayesian analysis · 2022Article
- Bayesian graphical models for modern biological applications.Statistical methods & applications · 2022Review
- Hierarchical Normalized Completely Random Measures for Robust Graphical Modeling.Bayesian analysis · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Gaussian graphical models are useful tools for exploring network structures in multivariate normal data. In this paper we are interested in situations where data show departures from Gaussianity, therefore requiring alternative modeling distributions. The multivariate
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What OpenQuestion holds
Registered trials
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.