Peter Grünwald
Peter Grünwald
Group Leader Machine Learning, CWI; Professor of Statistics, Leiden University
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The minimum description length principle
PD Grünwald
The MIT Press, 2007
A tutorial introduction to the minimum description length principle
P Grunwald
Advances in Minimum Description Length - Theory and Methods, 2005
Game theory, maximum entropy, minimum discrepancy and robust Bayesian decision theory
PD Grünwald, AP Dawid
Advances in Minimum Description Length: Theory and Applications
PD Grünwald, J Myung, MA Pitt
Advances in Minimum Description Length: Theory and Applications, 2005
Inconsistency of Bayesian inference for misspecified linear models, and a proposal for repairing it
P Grünwald, T Van Ommen
Shannon information and Kolmogorov complexity
P Grunwald, P Vitányi
arXiv preprint cs/0410002, 2004
Model selection based on minimum description length
P Grünwald
Journal of mathematical psychology 44 (1), 133-152, 2000
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, 2014
Safe testing
P Grünwald, R de Heide, WM Koolen
arXiv preprint arxiv:1906.07801, 2020
The safe Bayesian
P Grünwald
International Conference on Algorithmic Learning Theory, 169-183, 2012
On discriminative Bayesian network classifiers and logistic regression
T Roos, H Wettig, P Grünwald, P Myllymäki, H Tirri
Machine Learning 59, 267-296, 2005
The minimum description length principle and reasoning under uncertainty
PD Grünwald
Ph.D. thesis, University of Amsterdam, 1998
A minimum description length approach to grammar inference
P Grünwald
International joint conference on artificial intelligence, 203-216, 1995
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
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
Fast rates in statistical and online learning
T Van Erven, P Grunwald, NA Mehta, M Reid, R Williamson
Journal of Machine Learning Research 16, 1793-1861, 2015
Fast rates for general unbounded loss functions: from ERM to generalized Bayes
PD Grünwald, NA Mehta
Journal of Machine Learning Research 21 (56), 1-80, 2020
The statistical strength of nonlocality proofs
W Van Dam, RD Gill, PD Grunwald
IEEE transactions on information theory 51 (8), 2812-2835, 2005
Suboptimal behavior of Bayes and MDL in classification under misspecification
P Grünwald, J Langford
Machine Learning 66, 119-149, 2007
Catching up faster by switching sooner: a predictive approach to adaptive estimation with an application to the AIC–BIC dilemma
T Erven, P Grünwald, S De Rooij
Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2012
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Artículos 1–20