Aki Vehtari

21.3k total citations · 7 hit papers
153 papers, 11.0k citations indexed

About

Aki Vehtari is a scholar working on Artificial Intelligence, Statistics and Probability and Control and Systems Engineering. According to data from OpenAlex, Aki Vehtari has authored 153 papers receiving a total of 11.0k indexed citations (citations by other indexed papers that have themselves been cited), including 73 papers in Artificial Intelligence, 56 papers in Statistics and Probability and 20 papers in Control and Systems Engineering. Recurrent topics in Aki Vehtari's work include Gaussian Processes and Bayesian Inference (45 papers), Statistical Methods and Inference (36 papers) and Statistical Methods and Bayesian Inference (34 papers). Aki Vehtari is often cited by papers focused on Gaussian Processes and Bayesian Inference (45 papers), Statistical Methods and Inference (36 papers) and Statistical Methods and Bayesian Inference (34 papers). Aki Vehtari collaborates with scholars based in Finland, United States and Germany. Aki Vehtari's co-authors include Andrew Gelman, Jonah Gabry, Jessica Hwang, Jouko Lampinen, Juho Piironen, Ben Goodrich, Daniel Simpson, Simo Särkkä, Michael Betancourt and Jaakko Riihimäki and has published in prestigious journals such as Nature Communications, The Journal of Chemical Physics and SHILAP Revista de lepidopterología.

In The Last Decade

Aki Vehtari

143 papers receiving 10.7k citations

Hit Papers

5 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC

2016 3.1k citations Aki Vehtari, Andrew Gelman et al. Statistics and Computing profile →
Understanding predictive information criteria for Bayesian models

2013 1.3k citations Andrew Gelman, Aki Vehtari et al. Statistics and Computing profile →
Risk of recurrence of gastrointestinal stromal tumour after surgery: an analysis of pooled population-based cohorts

2011 673 citations Aki Vehtari, Jaakko Riihimäki et al. profile →
Visualization in Bayesian Workflow

2019 604 citations Aki Vehtari, Andrew Gelman et al. profile →
R-squared for Bayesian Regression Models

2018 563 citations Andrew Gelman, Ben Goodrich et al. profile →
Regression and Other Stories

2020 333 citations Andrew Gelman, Aki Vehtari et al. profile →
Sparsity information and regularization in the horseshoe and other shrinkage priors

2017 250 citations Juho Piironen, Aki Vehtari profile →

Peers

Aki Vehtari

AI

SP

ECOLO

CN

GPC

Martyn Plummer France

J A Hanley Canada

Sabine Landau United Kingdom

Sture Holm Sweden

Paul H.C. Eilers Netherlands

Alan Agresti United States

George Casella United States

David Clayton United Kingdom

Kenneth Lange United States

W. J. Conover United States

Martyn Plummer France

Aki Vehtari

814 ×0.4

AI

1.2k ×0.8

SP

2.1k ×1.7

ECOLO

256 ×0.3

CN

993 ×1.1

GPC

Citations per year, relative to Aki Vehtari Aki Vehtari (= 1×) peers Martyn Plummer

Countries citing papers authored by Aki Vehtari

Since Specialization

Citations

This map shows the geographic impact of Aki Vehtari's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Aki Vehtari with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aki Vehtari more than expected).

Fields of papers citing papers by Aki Vehtari

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Aki Vehtari. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Aki Vehtari. The network helps show where Aki Vehtari may publish in the future.

Co-authorship network of co-authors of Aki Vehtari

This figure shows the co-authorship network connecting the top 25 collaborators of Aki Vehtari. A scholar is included among the top collaborators of Aki Vehtari based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Aki Vehtari. Aki Vehtari is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Ghosh, Kunal, Milica Todorović, Aki Vehtari, & Patrick Rinke. (2025). Active learning of molecular data for task-specific objectives. The Journal of Chemical Physics. 162(1). 2 indexed citations

2.

Margossian, Charles C., et al.. (2024). Nested Rˆ: Assessing the Convergence of Markov Chain Monte Carlo When Running Many Short Chains. Bayesian Analysis. 20(4). 1 indexed citations

3.

Goodrich, Ben, et al.. (2024). The Piranha Problem: Large Effects Swimming in a Small Pond. Notices of the American Mathematical Society. 72(1). 1–1. 1 indexed citations

4.

Weber, Frank, Änne Glass, & Aki Vehtari. (2024). Projection predictive variable selection for discrete response families with finite support. Computational Statistics. 40(2). 701–721. 2 indexed citations

5.

Vehtari, Aki, et al.. (2024). Efficient estimation and correction of selection-induced bias with order statistics. Statistics and Computing. 34(4). 1 indexed citations

6.

Štrumbelj, Erik, Alexandre Bouchard‐Côté, Jukka Corander, et al.. (2024). Past, Present and Future of Software for Bayesian Inference. Statistical Science. 39(1). 14 indexed citations

7.

Monnahan, Cole C., et al.. (2021). Accounting for spatial dependence improves relative abundance estimates in a benthic marine species structured as a metapopulation. Fisheries Research. 240. 105960–105960. 3 indexed citations

8.

Paananen, Topi, Juho Piironen, Paul‐Christian Bürkner, & Aki Vehtari. (2021). Implicitly adaptive importance sampling. Statistics and Computing. 31(2). 20 indexed citations

9.

Kantonen, Tatu, Tomi Karjalainen, Janne Isojärvi, et al.. (2020). Interindividual variability and lateralization of μ-opioid receptors in the human brain. NeuroImage. 217. 116922–116922. 66 indexed citations

10.

Ghosh, Kunal, Annika Stuke, Milica Todorović, et al.. (2019). Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra. Advanced Science. 6(9). 1801367–1801367. 190 indexed citations

11.

Peltola, Tomi, et al.. (2019). Making Bayesian Predictive Models Interpretable: A Decision Theoretic Approach. arXiv (Cornell University). 1 indexed citations

12.

Järvenpää, Marko, Michael U. Gutmann, Aki Vehtari, & Pekka Marttinen. (2019). Parallel Gaussian process surrogate method to accelerate likelihood-free inference. arXiv (Cornell University). 1 indexed citations

13.

Karppinen, Jaro, Aki Vehtari, Satu Luoto, et al.. (2018). Effectiveness of three interventions for secondary prevention of low back pain in the occupational health setting:a randomised controlled trial with a natural course control. University of Oulu Repository (University of Oulu). 22 indexed citations

14.

Andersen, Michael Riis, Aki Vehtari, Ole Winther, & Lars Kai Hansen. (2017). Bayesian Inference for Spatio-temporal Spike-and-Slab Priors. Journal of Machine Learning Research. 18(139). 1–58. 17 indexed citations

15.

Heikkonen, Jukka, Jari Varjo, & Aki Vehtari. (2015). Forest change detection via Landsat TM difference features. Jukuri (Natural Resources Institute Finland (Luke)).

16.

Peltola, Tomi, Pasi Jylänki, & Aki Vehtari. (2014). Expectation Propagation for Likelihoods Depending on an Inner Product of Two Multivariate Random Variables. Radboud Repository (Radboud University). 33. 769–777. 1 indexed citations

17.

Riihimäki, Jaakko & Aki Vehtari. (2012). Laplace approximation for logistic Gaussian process density estimation. arXiv (Cornell University). 2 indexed citations

18.

Vanhatalo, Jarno, Pasi Jylänki, & Aki Vehtari. (2009). Gaussian process regression with Student-t likelihood. Neural Information Processing Systems. 22. 1910–1918. 57 indexed citations

19.

Auranen, Toni, Aapo Nummenmaa, Matti Hämäläinen, et al.. (2007). Bayesian inverse analysis of neuromagnetic data using cortically constrained multiple dipoles. Human Brain Mapping. 28(10). 979–994. 13 indexed citations

20.

Vehtari, Aki. (2002). Discussion to "Bayesian measures of model complexity and fit" by Spiegelhalter, D.J., Best, N.G., Carlin, B.P., and van der Linde, A.. 64(4). 274 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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