Evidence map›Paper›PMID 40226303›Full record

ArticleBiometrika2024

Radial Neighbors for Provably Accurate Scalable Approximations of Gaussian Processes.

Yichen Zhu, Michele Peruzzi, Cheng Li, David B Dunson

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biometrika, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
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

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Spatial meshing for general Bayesian multivariate models.Journal of machine learning research : JMLR · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yichen ZhuBocconi Institute for Data Science and Analytics, Bocconi University, Milan, MI, Italy.
Michele PeruzziDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan, U.S.A.
Cheng LiDepartment of Statistics and Data Science, National University of Singapore, Sinagpore.
David B DunsonDepartment of Statistical Science & Mathematics, Duke University, North Carolina, U.S.A.

Funding

Structured nonparametric methods for mixtures of exposuresR01ES028804 · NIEHS · DUKE UNIVERSITY · PI DUNSON, DAVID BRIAN · 2018 to 2021
$2.2M
Improving inferences on health effects of chemical exposuresR01ES035625 · NIEHS · DUKE UNIVERSITY · PI David Brian Dunson · 2023 to 2026
$1.6M
NIEHS NIH HHS R01 ES028804NIEHS NIH HHS R01 ES035625
6 · The paper itself

Abstract

In geostatistical problems with massive sample size, Gaussian processes can be approximated using sparse directed acyclic graphs to achieve scalable

Indexed as

ApproximationsDirected acyclic graphGaussian processSpatial statisticsWasserstein distance

Identifiers

PMID40226303
PMCPMC11993192

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.