ArticleJournal of machine learning research : JMLR2025
Bayesian Multi-Group Gaussian Process Models for Heterogeneous Group-Structured Data.
Article in Journal of machine learning research : JMLR, 2025. 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.
- Scalable multi-group nonnegative spatial factorization for spatial genomics data with cell-type heterogeneity.bioRxiv : the preprint server for biology · 2026Article
- A multilevel hierarchical framework for quantification of experimental heterogeneity in population snapshot data.PLoS computational biology · 2026Article
- Parametric Bayesian modelling of tuberculosis mortality determinants and facility level heterogeneity effect using Gamma and Gaussian shared frailty techniques.BMC infectious diseases · 2026Article
- Gaussian processes for time series with lead-lag effects with applications to biology data.Biometrics · 2025Article
- A latent scale model to minimize subjectivity in the analysis of visual rating data for the National Turfgrass Evaluation Program.Frontiers in plant science · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Gaussian processes are pervasive in functional data analysis, machine learning, and spatial statistics for modeling complex dependencies. Scientific data are often heterogeneous in their inputs and contain multiple known discrete groups of samples; thus, it is desirable to leverage the similarity among groups while accounting for heterogeneity across groups. We propose multi-group Gaussian processes (MGGPs) defined over
Indexed as
Identifiers
41019101PMC12463451What 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.