Evidence map›Paper›PMID 36570797›Full record

ArticleJournal of the Royal Statistical Society. Series B, Statistical methodology2022

Fast increased fidelity samplers for approximate Bayesian Gaussian process regression.

Kelly R Moran, Matthew W Wheeler

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the Royal Statistical Society. Series B, Statistical methodology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 33% of its field
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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. A review of geospatial exposure models and approaches for health data integration.Journal of exposure science & environmental epidemiology · 2025
    Review
  2. Spatial Multivariate Trees for Big Data Bayesian Regression.Journal of machine learning research : JMLR · 2022
    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

2 authors at 2 institutions in 1 country.

Kelly R MoranLos Alamos National Laboratory, Los Alamos, USA.
Matthew W WheelerNIEHS, NIH, Research Triangle Park, USA.
Los Alamos National Laboratory · USTriangle · US

Funding

Non-parametric Bayesian Method DevelopmentZIAES103357 · NIEHS · NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES · PI WHEELER, MATTHEW · 2020 to 2024
$546k
Intramural NIH HHS Z99 ES999999Intramural NIH HHS ZIA ES103357
6 · The paper itself

Abstract

Gaussian processes (GPs) are common components in Bayesian non-parametric models having a rich methodological literature and strong theoretical grounding. The use of exact GPs in Bayesian models is limited to problems containing several thousand observations due to their prohibitive computational demands. We develop a posterior sampling algorithm using

Indexed as

Bayesian Gaussian processMatrix compressionScalabilitySurface estimationTensor product

Identifiers

PMID36570797
PMCPMC9770094
OpenAlexW4224312890

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.