Evidence map›Paper›PMID 32471948›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2020

The frontier of simulation-based inference.

Kyle Cranmer, Johann Brehmer, Gilles Louppe

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 200 papers.

0numbers the graph read from it
0cells of the map it votes in
200citing 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

200 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Decomposing response inhibition: A POMDP model.PLoS computational biology · 2026
    Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. Article

140 more citing papers are in PubMed but not listed here.

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.

Kyle CranmerCenter for Cosmology and Particle Physics, New York University, New York, NY 10003; kyle.cranmer@nyu.edu.ORCID 0000-0002-5769-7094
Johann BrehmerCenter for Cosmology and Particle Physics, New York University, New York, NY 10003.ORCID 0000-0003-3344-4209
Gilles LouppeMontefiore Institute, University of Liège, B-4000 Liège, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many domains of science have developed complex simulations to describe phenomena of interest. While these simulations provide high-fidelity models, they are poorly suited for inference and lead to challenging inverse problems. We review the rapidly developing field of simulation-based inference and identify the forces giving additional momentum to the field. Finally, we describe how the frontier is expanding so that a broad audience can appreciate the profound influence these developments may have on science.

Indexed as

approximate Bayesian computationimplicit modelslikelihood-free inferenceneural density estimationstatistical inference

Identifiers

PMID32471948
PMCPMC7720103

What OpenQuestion holds

Textmetadata
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.