Aki Vehtari

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Andrew GelmanProfessor of Statistics and Political Science, Columbia UniversityVerified email at stat.columbia.edu
Jonah GabryColumbia UniversityVerified email at columbia.edu
Paul-Christian BürknerFull Professor of Computational Statistics, TU Dortmund UniversityVerified email at tu-dortmund.de
Jouko LampinenAalto UniversityVerified email at aalto.fi
Daniel SimpsonNormal ComputingVerified email at normalcomputing.ai
Juho PiironenLead scientist, Ph.D., Top Data Science companyVerified email at topdatascience.com
Yuling YaoUT AustinVerified email at flatironinstitute.org
Donald B RubinYCMS Tsinghua; Senior Research Fellow at Fox / Temple U, Emeritus at Harvard UVerified email at temple.edu
John B. CarlinMurdoch Childrens Research Institute, University of MelbourneVerified email at mcri.edu.au
Hal SternProfessor of Statistics, University of California, IrvineVerified email at uci.edu
Jarno VanhataloAssociate professor, University of HelsinkiVerified email at helsinki.fi
Michael Riis AndersenAssociate professor, Technical University of DenmarkVerified email at dtu.dk
Måns MagnussonDepartment of Statistics, Uppsala University, SwedenVerified email at statistik.uu.se
Simo SärkkäProfessor, Aalto UniversityVerified email at aalto.fi
Toni AuranenStaff Scientist, Aalto NeuroImaging, Aalto UniversityVerified email at aalto.fi
Bob CarpenterFlatiron Institute, Center for Computational MathematicsVerified email at flatironinstitute.org
Iiro P JaaskelainenFull Professor of Systems Neuroscience in Aalto University, Espoo, FinlandVerified email at aalto.fi
Mikko SamsProfessor of Cognitive Neuroscience, Aalto UniversityVerified email at aalto.fi
Samuel KaskiProfessor, Aalto University and University of ManchesterVerified email at aalto.fi
Charles C MargossianResearch Fellow, Flatiron InstituteVerified email at flatironinstitute.org

Aki Vehtari

Professor, Aalto University

Verified email at aalto.fi - Homepage

Bayesian analysis Bayesian statistics Bayesian workflow Gaussian processes


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Bayesian data analysis, 3rd edition A Gelman, JB Carlin, HS Stern, DB Dunson, A Vehtari, DB Rubin Chapman and Hall/CRC, 2013	39916*	2013
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC A Vehtari, A Gelman, J Gabry Statistics and computing 27, 1413-1432, 2017	5093	2017
Understanding predictive information criteria for Bayesian models A Gelman, J Hwang, A Vehtari Statistics and computing 24, 997-1016, 2014	2318	2014
Rank-normalization, folding, and localization: An improved for assessing convergence of MCMC A Vehtari, A Gelman, D Simpson, B Carpenter, PC Bürkner Bayesian analysis 16 (2), 667-718, 2021	1371*	2021
One vs three years of adjuvant imatinib for operable gastrointestinal stromal tumor: a randomized trial H Joensuu, M Eriksson, KS Hall, JT Hartmann, D Pink, J Schütte, ... Jama 307 (12), 1265-1272, 2012	1186	2012
Risk of recurrence of gastrointestinal stromal tumour after surgery: an analysis of pooled population-based cohorts H Joensuu, A Vehtari, J Riihimäki, T Nishida, SE Steigen, P Brabec, ... The lancet oncology 13 (3), 265-274, 2012	1091	2012
Visualization in Bayesian workflow J Gabry, D Simpson, A Vehtari, M Betancourt, A Gelman Journal of the Royal Statistical Society Series A, 2017	947	2017
Regression and other stories A Gelman, J Hill, A Vehtari Cambridge University Press, 2021	847*	2021
loo: Efficient leave-one-out cross-validation and WAIC for Bayesian models A Vehtari, J Gabry, M Magnusson, Y Yao, PC Bürkner, T Paananen, ... CRAN: Contributed Packages, 2015	821*	2015
R-squared for Bayesian regression models A Gelman, B Goodrich, J Gabry, A Vehtari The American Statistician 73 (3), 307-309, 2019	803	2019
Using stacking to average Bayesian predictive distributions Y Yao, A Vehtari, D Simpson, A Gelman Bayesian Analysis 13 (3), 917-1003, 2018	610	2018
Bayesian approach for neural networks—review and case studies J Lampinen, A Vehtari Neural networks 14 (3), 257-274, 2001	484	2001
Sparsity information and regularization in the horseshoe and other shrinkage priors J Piironen, A Vehtari Electronic Journal of Statistics 11 (2), 5018-5051, 2017	476	2017
A survey of Bayesian predictive methods for model assessment, selection and comparison A Vehtari, J Ojanen Statistics Surveys 6, 142-228, 2012	433	2012
Rao-Blackwellized particle filter for multiple target tracking S Särkkä, A Vehtari, J Lampinen Information Fusion 8 (1), 2-15, 2007	393	2007
Bayesian workflow A Gelman, A Vehtari, D Simpson, CC Margossian, B Carpenter, Y Yao, ... arXiv preprint arXiv:2011.01808, 2020	384*	2020
Comparison of Bayesian predictive methods for model selection J Piironen, A Vehtari Statistics and Computing 27, 711-735, 2017	384	2017
Pareto smoothed importance sampling A Vehtari, D Simpson, A Gelman, Y Yao, J Gabry Journal of Machine Learning Research 25 (72), 1-58, 2024	360*	2024
GPstuff: Bayesian modeling with Gaussian processes J Vanhatalo, J Riihimäki, J Hartikainen, P Jylänki, V Tolvanen, A Vehtari Journal of Machine Learning Research 14, 1175-1179, 2013	355*	2013
Deep learning spectroscopy: Neural networks for molecular excitation spectra K Ghosh, A Stuke, M Todorović, PB Jørgensen, MN Schmidt, A Vehtari, ... Advanced science 6 (9), 1801367, 2019	260	2019

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