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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy
Paul‐Christian Bürkner, Maximilian Scholz, Stefan T. Radev
Statistics Surveys (2023) Vol. 17, Iss. none
Open Access | Times Cited: 9

Showing 9 citing articles:

Is Ockham’s razor losing its edge? New perspectives on the principle of model parsimony
Marina Dubova, Suyog Chandramouli, Gerd Gigerenzer, et al.
Proceedings of the National Academy of Sciences (2025) Vol. 122, Iss. 5
Open Access | Times Cited: 1
Deep Learning and Likelihood Approaches for Viral Phylogeography Converge on the Same Answers Whether the Inference Model Is Right or Wrong
Ammon Thompson, Benjamin J. Liebeskind, Erik J. Scully, et al.
Systematic Biology (2024) Vol. 73, Iss. 1, pp. 183-206
Open Access | Times Cited: 13
Towards more appropriate modelling of linguistic complexity measures: Beyond traditional regression models
Akira Murakami
Research Methods in Applied Linguistics (2025) Vol. 4, Iss. 1, pp. 100182-100182
Open Access
Uncertainty quantification and propagation in surrogate-based Bayesian inference
Philipp Reiser, Javier Enrique Aguilar, Anneli Guthke, et al.
Statistics and Computing (2025) Vol. 35, Iss. 3
Open Access
Is Ockham’s razor losing its edge? New perspectives on the principle of model parsimony
Marina Dubova, Suyog Chandramouli, Gerd Gigerenzer, et al.
(2024)
Open Access | Times Cited: 4
Deep learning and likelihood approaches for viral phylogeography converge on the same answers whether the inference model is right or wrong
Ammon Thompson, Benjamin J. Liebeskind, Erik J. Scully, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 11
BayesFlow: Amortized Bayesian Workflows With Neural Networks
Stefan T. Radev, Marvin Schmitt, Lukas Schumacher, et al.
The Journal of Open Source Software (2023) Vol. 8, Iss. 89, pp. 5702-5702
Open Access | Times Cited: 9
Computational mechanisms underlying latent value updating of unchosen actions
Ido Ben-Artzi, Yoav Kessler, Bruno Nicenboim, et al.
Science Advances (2023) Vol. 9, Iss. 42
Open Access | Times Cited: 7
A Bayesian approach to (re)examining learning effects of cognitive linguistics–inspired instruction
Man Ho Ivy Wong, Jakob Prange
Studies in Second Language Acquisition (2024), pp. 1-21
Open Access | Times Cited: 2
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