ArticleNeural computation2019
Learning Moral Graphs in Construction of High-Dimensional Bayesian Networks for Mixed Data.
Article in Neural computation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Causal discovery methods in psychological research: Foundations, algorithms, and a practical tutorial in R.Behavior research methods · 2026Review
- Time-varying dynamic Bayesian network learning for an fMRI study of emotion processing.Statistics in medicine · 2024Article
- A Double Regression Method for Graphical Modeling of High-dimensional Nonlinear and Non-Gaussian Data.Statistics and its interface · 2024Article
- Markov neighborhood regression for statistical inference of high-dimensional generalized linear models.Statistics in medicine · 2022Article
- Fast hybrid Bayesian integrative learning of multiple gene regulatory networks for type 1 diabetes.Biostatistics (Oxford, England) · 2021Article
- Article
- Markov Neighborhood Regression for High-Dimensional Inference.Journal of the American Statistical Association · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Bayesian networks have been widely used in many scientific fields for describing the conditional independence relationships for a large set of random variables. This letter proposes a novel algorithm, the so-called
Indexed as
Identifiers
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.