Evidence map›Paper›PMID 33456293›Full record

ArticleJournal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America2020

Consensus Monte Carlo for Random Subsets using Shared Anchors.

Yang Ni, Yuan Ji, Peter Müller

Open access · greenAbstract read
In one paragraph

Article in Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.9field-weighted citation impact, top 11% 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

3 citing papers in PubMed, 17 citations in OpenAlex.

  1. Federated Learning for Sparse Bayesian Models with Applications to Electronic Health Records and Genomics.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2023
    Article
  2. Article
  3. 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

3 authors at 3 institutions in 1 country.

Yang NiDepartment of Statistics, Texas A&M University.
Yuan JiDepartment of Public Health Sciences, The University of Chicago.
Peter MüllerDepartment of Mathematics, The University of Texas at Austin.
Chicago Department of Public Health · USTexas A&M University · USThe University of Texas at Austin · US

Funding

Bayesian models for cancer prognosis by integrating diverse types of dataR01CA132897 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI JI, YUAN, MUELLER, PETER · 2008 to 2019
$2.1M
NCI NIH HHS R01 CA132897
6 · The paper itself

Abstract

We present a consensus Monte Carlo algorithm that scales existing Bayesian nonparametric models for clustering and feature allocation to big data. The algorithm is valid for any prior on random subsets such as partitions and latent feature allocation, under essentially any sampling model. Motivated by three case studies, we focus on clustering induced by a Dirichlet process mixture sampling model, inference under an Indian buffet process prior with a binomial sampling model, and with a categorical sampling model. We assess the proposed algorithm with simulation studies and show results for inference with three datasets: an MNIST image dataset, a dataset of pancreatic cancer mutations, and a large set of electronic health records (EHR). Supplementary materials for this article are available online.

Indexed as

Big dataelectronic health recordsimage clusterparallel computingtumor heterogeneity

Identifiers

PMID33456293
PMCPMC7810350
OpenAlexW3010567980

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.