Evidence map›Paper›PMID 40995377›Full record

ArticleBayesian analysis2025

Quantum Speedups for Multiproposal MCMC.

Chin-Yi Lin, Kuo-Chin Chen, Philippe Lemey, Marc A Suchard, Andrew J Holbrook, Min-Hsiu Hsieh

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bayesian analysis, 2025. 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. 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

6 authors.

Chin-Yi LinDepartment of Physics, National Taiwan University, Taipei, Taiwan.
Kuo-Chin ChenFoxconn Research, Taipei, Taiwan.
Philippe LemeyDepartment of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium.
Marc A SuchardDepartments of Biostatistics, Biomathematics and Human Genetics, UCLA, Los Angeles, USA.
Andrew J HolbrookDepartment of Biostatistics, UCLA, Los Angeles, USA.
Min-Hsiu HsiehFoxconn Research, Taipei, Taiwan.

Funding

Fast and flexible Bayesian phylogenetics via modern machine learningR01AI162611 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI MATSEN, FREDERICK ALBERT · 2021 to 2025
$3.8M
Statistical Innovation to Integrate Sequences and Phenotypes for Scalable Phylodynamic InferenceR01AI153044 · NIAID · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Marc A. Suchard · 2021 to 2026
$2.4M
Big Data Predictive Phylogenetics with Bayesian LearningK25AI153816 · NIAID · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI HOLBROOK, ANDREW JAMES · 2020 to 2024
$532k
NIAID NIH HHS K25 AI153816NIAID NIH HHS R01 AI153044NIAID NIH HHS R01 AI162611Wellcome Trust
6 · The paper itself

Abstract

Multiproposal Markov chain Monte Carlo (MCMC) algorithms choose from multiple proposals to generate their next chain step in order to sample from challenging target distributions more efficiently. However, on classical machines, these algorithms require

Indexed as

Bayesian phylogeneticsIsing modelsMCMCquantum algorithms

Identifiers

PMID40995377
PMCPMC12456418

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.