Peter Grünwald

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Tim van ErvenAssociate professor at the University of Amsterdam, the NetherlandsVerified email at uva.nl
Petri MyllymakiProfessor of Computer Science, University of HelsinkiVerified email at helsinki.fi
Wouter M. KoolenCentrum Wiskunde & InformaticaVerified email at cwi.nl
Teemu RoosProfessor at University of HelsinkiVerified email at cs.helsinki.fi
Nishant A. MehtaAssistant Professor, University of VictoriaVerified email at uvic.ca
Paul VitanyiCWI and University of AmsterdamVerified email at cwi.nl
Jay MyungProfessor, Department of Psychology, Ohio State UniversityVerified email at osu.edu
Mark PittProfessor, Ohio State UniversityVerified email at osu.edu
Wojciech KotlowskiPoznan University of TechnologyVerified email at cs.put.poznan.pl
Joseph HalpernProfessor of Computer Science, Cornell UniversityVerified email at cs.cornell.edu
Thijs van OmmenAssistant Professor at Utrecht UniversityVerified email at uu.nl
Philip DawidEmeritus Professor of Statistics, University of CambridgeVerified email at statslab.cam.ac.uk
Rianne de HeidePostdoc, Leiden University & Otto von Guericke Universität MagdeburgVerified email at cwi.nl
Richard D. GillProfessor of Mathematical Statistics, Leiden UniversityVerified email at math.leidenuniv.nl
Eric-Jan WagenmakersUniversity of AmsterdamVerified email at uva.nl
Robert C. WilliamsonUniversity of TübingenVerified email at uni-tuebingen.de
Mark D. ReidAppleVerified email at reid.name
Wim van DamProfessor of Computer Science and Physics, UC Santa BarbaraVerified email at ucsb.edu
Mark SteyversUniversity of California, IrvineVerified email at uci.edu
Stéphanie van der PasAssistant professor, Amsterdam UMCVerified email at amsterdamumc.nl

Peter Grünwald

Group Leader Machine Learning, CWI; Professor of Statistics, Leiden University

Verified email at cwi.nl - Homepage

machine learning statistics information theory foundations of probability statistics and the law


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
The minimum description length principle PD Grünwald The MIT Press, 2007	1568	2007
A tutorial introduction to the minimum description length principle P Grunwald arXiv preprint math/0406077, 2004	539	2004
Game theory, maximum entropy, minimum discrepancy and robust Bayesian decision theory PD Grünwald, AP Dawid Annals of statistics 32 (4), 1367-1433, 2004	459	2004
Advances in Minimum Description Length: Theory and Applications PD Grünwald, J Myung, MA Pitt Advances in Minimum Description Length: Theory and Applications, 2005	425	2005
Model selection based on minimum description length P Grünwald Journal of mathematical psychology 44 (1), 133-152, 2000	201	2000
Shannon information and Kolmogorov complexity P Grunwald, P Vitányi arXiv preprint cs/0410002, 2004	171	2004
The minimum description length principle and reasoning under uncertainty PD Grünwald Ph.D. thesis, University of Amsterdam, 1998	163	1998
On discriminative Bayesian network classifiers and logistic regression T Roos, H Wettig, P Grünwald, P Myllymäki, H Tirri Machine Learning 59 (3), 267-296, 2005	148	2005
Follow the leader if you can, hedge if you must S de Rooij, T Van Erven, PD Grünwald, WM Koolen Journal of Machine Learning Research (JMLR) 15, 1281-1315, 2013	147	2013
A minimum description length approach to grammar inference P Grünwald International Joint Conference on Artificial Intelligence, 203-216, 1995	139	1995
Inconsistency of Bayesian inference for misspecified linear models, and a proposal for repairing it P Grünwald, T Van Ommen Bayesian Analysis 12 (4), 1069-1103, 2017	131	2017
Kolmogorov complexity and information theory. With an interpretation in terms of questions and answers PD Grünwald, PMB Vitányi Journal of Logic, Language and Information 12 (4), 497-529, 2003	114	2003
Accumulative prediction error and the selection of time series models EJ Wagenmakers, P Grünwald, M Steyvers Journal of Mathematical Psychology 50 (2), 149-166, 2006	112	2006
The safe bayesian P Grünwald International Conference on Algorithmic Learning Theory, 169-183, 2012	88	2012
Catching Up Faster by Switching Sooner: A predictive approach to adaptive estimation with an application to the AIC-BIC Dilemma T van Erven, P Grünwald, S de Rooij Journal of the Royal Statistical Society, Series B, 2011	87*	2011
The statistical strength of nonlocality proofs W Van Dam, RD Gill, PD Grunwald IEEE transactions on information theory 51 (8), 2812-2835, 2005	85	2005
Suboptimal behavior of Bayes and MDL in classification under misspecification P Grünwald, J Langford Machine Learning 66 (2-3), 119-149, 2007	84*	2007
Updating probabilities P Grünwald, JY Halpern J. Artif. Intell. Res.(JAIR) 19, 243-278, 2003	84	2003
Fast rates in statistical and online learning T Van Erven, P Grunwald, NA Mehta, M Reid, R Williamson MIT Press, 2015	68	2015
Comparing predictive inference methods for discrete domains P Kontkanen, P Myllymäki, T Silander, H Tirri, P Grünwald Sixth International Workshop on Artificial Intelligence and Statistics, 311-318, 1997	66	1997

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