Aki Vehtari - Google Scholar Citations

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	All	Since 2014
Citations	32652	16946
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Co-authors

Andrew GelmanProfessor of Statistics and Political Science, Columbia UniversityVerified email at stat.columbia.edu
Jouko LampinenAalto UniversityVerified email at aalto.fi
Jarno VanhataloAssistant professor, University of HelsinkiVerified email at helsinki.fi
Simo SärkkäAssociate Professor, 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
Juho PiironenResearcher, D.Sc., Curious AI companyVerified email at aalto.fi
Toni AuranenStaff Scientist, Aalto NeuroImaging, Aalto UniversityVerified email at aalto.fi
Iiro P JaaskelainenAssociate Professor, Aalto University School of Science, Espoo, FinlandVerified email at aalto.fi
Daniel SimpsonUniversity of TorontoVerified email at utstat.toronto.edu
Mikko SamsProfessor of Cognitive Neuroscience, Aalto UniversityVerified email at aalto.fi
Matti HamalainenMassachusetts General HospitalVerified email at nmr.mgh.harvard.edu
Tomi PeltolaPostdoctoral researcher, Aalto UniversityVerified email at aalto.fi
Christian RobertUniversité Paris Dauphine, PSL & University of WarwickVerified email at ceremade.dauphine.fr
Michael U. GutmannUniversity of EdinburghVerified email at ed.ac.uk
Simo VanniClinical Neurosciences, Neurology; University of Helsinki and Helsinki University HospitalVerified email at helsinki.fi
Yuling YaoColumbia UniversityVerified email at columbia.edu
Marko JärvenpääDoctoral student, Aalto UniversityVerified email at aalto.fi

Aki Vehtari

Associate Professor, Aalto University

Verified email at aalto.fi - Homepage

Bayesian analysis Bayesian statistics Machine learning Gaussian processes probabilistic programming


Title	Cited by	Year
Bayesian data analysis A Gelman, JB Carlin, HS Stern, DB Dunson, A Vehtari, DB Rubin Chapman hall/CRC Press, 2013	25333	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	886	2017
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	827	2012
Understanding predictive information criteria for Bayesian models A Gelman, J Hwang, A Vehtari Statistics and computing 24 (6), 997-1016, 2014	792	2014
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	596	2012
Rao-Blackwellized particle filter for multiple target tracking S Särkkä, A Vehtari, J Lampinen Information Fusion 8 (1), 2-15, 2007	300	2007
Bayesian approach for neural networks—review and case studies J Lampinen, A Vehtari Neural networks 14 (3), 257-274, 2001	278	2001
A survey of Bayesian predictive methods for model assessment, selection and comparison A Vehtari, J Ojanen Statistics Surveys 6, 142-228, 2012	197	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	191	2013
Bayesian model assessment and comparison using cross-validation predictive densities A Vehtari, J Lampinen Neural computation 14 (10), 2439-2468, 2002	176	2002
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	118	2002
A multi-metabolite analysis of serum by 1H NMR spectroscopy: early systemic signs of Alzheimer’s disease T Tukiainen, T Tynkkynen, VP Mäkinen, P Jylänki, A Kangas, J Hokkanen, ... Biochemical and biophysical research communications 375 (3), 356-361, 2008	108	2008
Comparison of Bayesian predictive methods for model selection J Piironen, A Vehtari Statistics and Computing 27 (3), 711-735, 2017	106	2017
Gaussian processes with monotonicity information J Riihimäki, A Vehtari Proceedings of the Thirteenth International Conference on Artificial …, 2010	103	2010
Rao-Blackwellized Monte Carlo data association for multiple target tracking S Särkkä, A Vehtari, J Lampinen Proceedings of the seventh international conference on information fusion 1 …, 2004	101	2004
loo: Efficient leave-one-out cross-validation and WAIC for Bayesian models A Vehtari, A Gelman, J Gabry R package version 1 (0), 2016	96	2016
Robust Gaussian process regression with a Student-t likelihood P Jylänki, J Vanhatalo, A Vehtari Journal of Machine Learning Research 12 (Nov), 3227-3257, 2011	94	2011
Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER S Särkkä, A Solin, A Nummenmaa, A Vehtari, T Auranen, S Vanni, FH Lin NeuroImage 60 (2), 1517-1527, 2012	90	2012
Bayesian analysis of the neuromagnetic inverse problem with ℓp-norm priors T Auranen, A Nummenmaa, MS Hämäläinen, IP Jääskeläinen, ... NeuroImage 26 (3), 870-884, 2005	81	2005
Time series prediction by Kalman smoother with cross-validated noise density S Sarkka, A Vehtari, J Lampinen 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No …, 2004	79*	2004

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