Aki Vehtari

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Andrew GelmanProfessor of Statistics and Political Science, Columbia UniversityVerified email at stat.columbia.edu
Jouko LampinenAalto UniversityVerified email at aalto.fi
Jonah GabryColumbia UniversityVerified email at columbia.edu
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
Daniel SimpsonMonash UniversityVerified email at monash.edu
Juho PiironenSenior scientist, Ph.D., Top Data Science companyVerified email at topdatascience.com
Paul-Christian BuerknerJunior Research Group Leader, Cluster of Excellence SimTech, University of Stuttgart, GermanyVerified email at simtech.uni-stuttgart.de
Jarno VanhataloAssociate professor, University of HelsinkiVerified email at helsinki.fi
Yuling YaoFlatiron InstituteVerified email at flatironinstitute.org
Simo SärkkäAssociate Professor, Aalto UniversityVerified email at aalto.fi
Toni AuranenStaff Scientist, Aalto NeuroImaging, Aalto UniversityVerified email at aalto.fi
Michael Riis AndersenAssociate professor, Technical University of DenmarkVerified email at dtu.dk
Iiro P JaaskelainenVerified email at aalto.fi
Mikko SamsProfessor of Cognitive Neuroscience, Aalto UniversityVerified email at aalto.fi
Matti HamalainenMassachusetts General HospitalVerified email at mgh.harvard.edu
Christian RobertUniversité Paris Dauphine, PSL & University of WarwickVerified email at ceremade.dauphine.fr
Michael U. GutmannUniversity of EdinburghVerified email at ed.ac.uk
Marko JärvenpääPostdoc, University of OsloVerified email at medisin.uio.no

Aki Vehtari

Associate Professor, Aalto University

Verified email at aalto.fi - Homepage

Bayesian analysis Bayesian statistics Machine learning Gaussian processes probabilistic programming


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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	33386*	2013
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC A Vehtari, A Gelman, J Gabry Statistics and computing 27 (5), 1413-1432, 2017	2738	2017
Understanding predictive information criteria for Bayesian models A Gelman, J Hwang, A Vehtari Statistics and computing 24 (6), 997-1016, 2014	1578	2014
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	1011	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	849	2012
Visualization in Bayesian workflow J Gabry, D Simpson, A Vehtari, M Betancourt, A Gelman Journal of the Royal Statistical Society Series A, 2017	457	2017
loo: Efficient leave-one-out cross-validation and WAIC for Bayesian models A Vehtari, J Gabry, Y Yao, A Gelman R package version 2 (0), 1003, 2018	409	2018
R-squared for Bayesian regression models A Gelman, B Goodrich, J Gabry, A Vehtari The American Statistician, 2019	401	2019
Rank-normalization, folding, and localization: An improved ̂R for assessing convergence of MCMC A Vehtari, A Gelman, D Simpson, B Carpenter, PC Bürkner Bayesian analysis 16 (2), 667-718, 2021	381	2021
Rao-Blackwellized particle filter for multiple target tracking S Särkkä, A Vehtari, J Lampinen Information Fusion 8 (1), 2-15, 2007	348	2007
Bayesian approach for neural networks—review and case studies J Lampinen, A Vehtari Neural networks 14 (3), 257-274, 2001	342	2001
A survey of Bayesian predictive methods for model assessment, selection and comparison A Vehtari, J Ojanen Statistics Surveys 6, 142-228, 2012	322	2012
Using stacking to average Bayesian predictive distributions Y Yao, A Vehtari, D Simpson, A Gelman Bayesian Analysis 13 (3), 917-1003, 2018	307	2018
GPstuff: Bayesian modeling with Gaussian processes J Vanhatalo, J Riihimäki, J Hartikainen, P Jylänki, V Tolvanen, A Vehtari Journal of Machine Learning Research 14 (Apr), 1175-1179, 2013	277	2013
Comparison of Bayesian predictive methods for model selection J Piironen, A Vehtari Statistics and Computing 27 (3), 711-735, 2017	247	2017
Sparsity information and regularization in the horseshoe and other shrinkage priors J Piironen, A Vehtari Electronic Journal of Statistics 11 (2), 5018-5051, 2017	237	2017
Bayesian model assessment and comparison using cross-validation predictive densities A Vehtari, J Lampinen Neural computation 14 (10), 2439-2468, 2002	207	2002
Pareto smoothed importance sampling A Vehtari, D Simpson, A Gelman, Y Yao, J Gabry arXiv preprint arXiv:1507.02646, 2015	191	2015
Gaussian processes with monotonicity information J Riihimäki, A Vehtari Proceedings of the thirteenth international conference on artificial …, 2010	185	2010
Robust Gaussian Process Regression with a Student-t Likelihood. P Jylänki, J Vanhatalo, A Vehtari Journal of Machine Learning Research 12 (11), 2011	162	2011

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