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
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
Paul-Christian BuerknerJunior Research Group Leader, Cluster of Excellence SimTech, University of Stuttgart, GermanyVerified email at simtech.uni-stuttgart.de
Donald B RubinYCMS Tsinghua; Senior Research Fellow at Fox / Temple U, Emeritus at Harvard UVerified email at temple.edu
Juho PiironenSenior scientist, Ph.D., Top Data Science companyVerified email at topdatascience.com
Yuling YaoFlatiron InstituteVerified email at flatironinstitute.org
Jarno VanhataloAssociate professor, University of HelsinkiVerified email at helsinki.fi
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
Måns MagnussonDepartment of Statistics, Uppsala University, SwedenVerified email at statistik.uu.se
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
Matti HamalainenAalto UniversityVerified email at aalto.fi
Bob CarpenterFlatiron Institute, Center for Computational MathematicsVerified email at flatironinstitute.org
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 Bayesian workflow Machine learning 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	35288*	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	3367	2017
Understanding predictive information criteria for Bayesian models A Gelman, J Hwang, A Vehtari Statistics and computing 24 (6), 997-1016, 2014	1797	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	1068	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	925	2012
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	610	2021
Visualization in Bayesian workflow J Gabry, D Simpson, A Vehtari, M Betancourt, A Gelman Journal of the Royal Statistical Society Series A, 2017	605	2017
R-squared for Bayesian regression models A Gelman, B Goodrich, J Gabry, A Vehtari The American Statistician, 2019	515	2019
loo: Efficient leave-one-out cross-validation and WAIC for Bayesian models A Vehtari, J Gabry, M Magnusson, Y Yao, P Bürkner, T Paananen, ... R package version 2 (0), 1003, 2018	514	2018
Using stacking to average Bayesian predictive distributions Y Yao, A Vehtari, D Simpson, A Gelman Bayesian Analysis 13 (3), 917-1003, 2018	404*	2018
Bayesian approach for neural networks—review and case studies J Lampinen, A Vehtari Neural networks 14 (3), 257-274, 2001	377	2001
Rao-Blackwellized particle filter for multiple target tracking S Särkkä, A Vehtari, J Lampinen Information Fusion 8 (1), 2-15, 2007	365	2007
A survey of Bayesian predictive methods for model assessment, selection and comparison A Vehtari, J Ojanen	358	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, 1175-1179, 2013	327*	2013
Sparsity information and regularization in the horseshoe and other shrinkage priors J Piironen, A Vehtari	304	2017
Comparison of Bayesian predictive methods for model selection J Piironen, A Vehtari Statistics and Computing 27, 711-735, 2017	291	2017
Regression and other stories A Gelman, J Hill, A Vehtari Cambridge University Press, 2020	269	2020
Pareto smoothed importance sampling A Vehtari, D Simpson, A Gelman, Y Yao, J Gabry arXiv preprint arXiv:1507.02646, 2015	244	2015
Bayesian model assessment and comparison using cross-validation predictive densities A Vehtari, J Lampinen Neural computation 14 (10), 2439-2468, 2002	217	2002
Gaussian processes with monotonicity information J Riihimäki, A Vehtari Proceedings of the thirteenth international conference on artificial …, 2010	206	2010

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