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
Daniel SimpsonMonash UniversityVerified email at monash.edu
Juho PiironenSenior scientist, Ph.D., Top Data Science companyVerified email at topdatascience.com
Jarno VanhataloAssistant professor, University of HelsinkiVerified email at helsinki.fi
Simo SärkkäAssociate Professor, Aalto UniversityVerified email at aalto.fi
Yuling YaoColumbia UniversityVerified email at columbia.edu
Toni AuranenStaff Scientist, Aalto NeuroImaging, Aalto UniversityVerified email at aalto.fi
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
David DunsonArts & Sciences Professor of Statistical Science & MathematicsVerified email at duke.edu
Paul BuerknerJunior Research Group Leader, Cluster of Excellence SimTech, University of Stuttgart, GermanyVerified email at simtech.uni-stuttgart.de
Iiro P JaaskelainenAalto University, Espoo, Finland; Higher School of Economics, Moscow, RussiaVerified email at aalto.fi
Mikko SamsProfessor of Cognitive Neuroscience, Aalto UniversityVerified email at aalto.fi
Michael Riis AndersenAssistant professor, Technical University of DenmarkVerified email at dtu.dk
Matti HamalainenMassachusetts General HospitalVerified email at nmr.mgh.harvard.edu
Michael U. GutmannUniversity of EdinburghVerified email at ed.ac.uk
Christian RobertUniversité Paris Dauphine, PSL & University of WarwickVerified email at ceremade.dauphine.fr

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 A Gelman, JB Carlin, HS Stern, DB Dunson, A Vehtari, DB Rubin Chapman hall/CRC Press, 2013	30150	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	1939	2017
Understanding predictive information criteria for Bayesian models A Gelman, J Hwang, A Vehtari Statistics and computing 24 (6), 997-1016, 2014	1276	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	947	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	761	2012
Rao-Blackwellized particle filter for multiple target tracking S Särkkä, A Vehtari, J Lampinen Information Fusion 8 (1), 2-15, 2007	324	2007
Bayesian approach for neural networks—review and case studies J Lampinen, A Vehtari Neural networks 14 (3), 257-274, 2001	314	2001
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	289	2018
Visualization in Bayesian workflow J Gabry, D Simpson, A Vehtari, M Betancourt, A Gelman Journal of the Royal Statistical Society Series A, 2017	271	2017
A survey of Bayesian predictive methods for model assessment, selection and comparison A Vehtari, J Ojanen Statistics Surveys 6, 142-228, 2012	270	2012
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	262	2013
R-squared for Bayesian regression models A Gelman, B Goodrich, J Gabry, A Vehtari The American Statistician, 2019	234	2019
Comparison of Bayesian predictive methods for model selection J Piironen, A Vehtari Statistics and Computing 27 (3), 711-735, 2017	209	2017
Using stacking to average Bayesian predictive distributions Y Yao, A Vehtari, D Simpson, A Gelman Bayesian Analysis 13 (3), 917-1003, 2018	208	2018
Bayesian model assessment and comparison using cross-validation predictive densities A Vehtari, J Lampinen Neural computation 14 (10), 2439-2468, 2002	191	2002
Sparsity information and regularization in the horseshoe and other shrinkage priors J Piironen, A Vehtari Electronic Journal of Statistics 11 (2), 5018-5051, 2017	175	2017
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 1 (1), 1-28, 2021	165	2021
Gaussian processes with monotonicity information J Riihimäki, A Vehtari Proceedings of the thirteenth international conference on artificial …, 2010	162	2010
Robust Gaussian Process Regression with a Student-t Likelihood. P Jylänki, J Vanhatalo, A Vehtari Journal of Machine Learning Research 12 (11), 2011	149	2011
Discussion on the paper by Spiegelhalter, Best, Carlin and van der Linde SP Brooks, J Smith, A Vehtari, M Plummer, M Stone, CP Robert, ... Journal of the Royal Statistical Society. Series B: Statistical Methodology …, 2002	146	2002

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