Evidence map›Paper›PMID 39803594›Full record

ArticleStatistics & probability letters2025

On exact Bayesian credible sets for discrete parameters.

Chaegeun Song, Bing Li

Abstract read
In one paragraph

Article in Statistics & probability letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Chaegeun SongDepartment of Statistics, The Pennsylvania State University.
Bing LiDepartment of Statistics, The Pennsylvania State University.

Funding

Collaborative Research: DMS/NIGMS 2: New statistical methods, theory, and software for microbiome dataR01GM152812 · NIGMS · PENNSYLVANIA STATE UNIVERSITY, THE · PI Lingzhou Xue, Xiufan Yu · 2023 to 2026
$1.2M
NIGMS NIH HHS R01 GM152812
6 · The paper itself

Abstract

We introduce a generalized Bayesian credible set that can achieve any preassigned credible level, addressing a limitation of the current credible sets. This is achieved by exploiting a connection between the highest posterior density set and the Neyman-Pearson lemma.

Indexed as

Bayesian classificationHighest posterior density setNeyman-Pearson lemmaPattern recognition

Identifiers

PMID39803594
PMCPMC11722005

What OpenQuestion holds

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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.