Peter Grünwald

4.7k total citations
98 papers, 1.8k citations indexed

About

Peter Grünwald is a scholar working on Artificial Intelligence, Statistics and Probability and Management Science and Operations Research. According to data from OpenAlex, Peter Grünwald has authored 98 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Artificial Intelligence, 26 papers in Statistics and Probability and 18 papers in Management Science and Operations Research. Recurrent topics in Peter Grünwald's work include Bayesian Modeling and Causal Inference (19 papers), Machine Learning and Algorithms (18 papers) and Statistical Methods in Clinical Trials (13 papers). Peter Grünwald is often cited by papers focused on Bayesian Modeling and Causal Inference (19 papers), Machine Learning and Algorithms (18 papers) and Statistical Methods in Clinical Trials (13 papers). Peter Grünwald collaborates with scholars based in Netherlands, United States and Germany. Peter Grünwald's co-authors include A. P. Dawid, In Jae Myung, Mark A. Pitt, Petri Myllymäki, Kirsi Tirri, Joseph Y. Halpern, Steven de Rooij, Paul Vitányi, Wouter M. Koolen and Tim van Erven and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Statistical Association and Analytical Biochemistry.

In The Last Decade

Peter Grünwald

91 papers receiving 1.6k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Peter Grünwald Netherlands 21 923 329 262 166 156 98 1.8k
Jonas Peters Germany 26 1.7k 1.8× 550 1.7× 385 1.5× 261 1.6× 196 1.3× 51 2.6k
Dominik Janzing Germany 27 2.1k 2.3× 385 1.2× 247 0.9× 233 1.4× 322 2.1× 94 2.8k
Bertrand Clarke United States 13 535 0.6× 314 1.0× 93 0.4× 176 1.1× 107 0.7× 61 1.1k
Alessandro Rinaldo United States 20 565 0.6× 567 1.7× 75 0.3× 139 0.8× 203 1.3× 57 1.5k
Peter Harremoës Denmark 15 688 0.7× 245 0.7× 107 0.4× 86 0.5× 131 0.8× 61 1.7k
Thomas S. Richardson United States 21 880 1.0× 794 2.4× 159 0.6× 206 1.2× 121 0.8× 66 1.8k
Christopher Meek United States 15 1.6k 1.8× 123 0.4× 297 1.1× 233 1.4× 173 1.1× 41 2.5k
Alberto Suárez Spain 23 1.0k 1.1× 99 0.3× 261 1.0× 93 0.6× 139 0.9× 67 1.9k
Chee Lap Chow Singapore 12 1.4k 1.5× 199 0.6× 183 0.7× 173 1.0× 174 1.1× 24 2.7k
Yuguo Chen United States 22 453 0.5× 223 0.7× 99 0.4× 102 0.6× 138 0.9× 91 1.5k

Countries citing papers authored by Peter Grünwald

Since Specialization
Citations

This map shows the geographic impact of Peter Grünwald's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Peter Grünwald with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Grünwald more than expected).

Fields of papers citing papers by Peter Grünwald

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Peter Grünwald. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Peter Grünwald. The network helps show where Peter Grünwald may publish in the future.

Co-authorship network of co-authors of Peter Grünwald

This figure shows the co-authorship network connecting the top 25 collaborators of Peter Grünwald. A scholar is included among the top collaborators of Peter Grünwald based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Peter Grünwald. Peter Grünwald is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Gigerenzer, Gerd, Peter Grünwald, William R. Holmes, et al.. (2025). Is Ockham’s razor losing its edge? New perspectives on the principle of model parsimony. Proceedings of the National Academy of Sciences. 122(5). e2401230121–e2401230121. 5 indexed citations
2.
Grünwald, Peter, et al.. (2024). Safe testing. Journal of the Royal Statistical Society Series B (Statistical Methodology). 86(5). 1091–1128. 22 indexed citations
3.
Grünwald, Peter. (2024). Beyond Neyman–Pearson: E-values enable hypothesis testing with a data-driven alpha. Proceedings of the National Academy of Sciences. 121(39). e2302098121–e2302098121. 3 indexed citations
4.
Grünwald, Peter, et al.. (2021). The no-free-lunch theorems of supervised learning. Synthese. 199(3-4). 9979–10015. 45 indexed citations
5.
Erven, Tim van, Peter Grünwald, Nishant A. Mehta, Mark D. Reid, & Robert C. Williamson. (2015). Fast rates in statistical and online learning. Journal of Machine Learning Research. 16(1). 1793–1861. 18 indexed citations
6.
Koolen, Wouter M., Tim van Erven, & Peter Grünwald. (2014). Learning the Learning Rate for Prediction with Expert Advice. QUT ePrints (Queensland University of Technology). 27. 2294–2302. 3 indexed citations
7.
Grünwald, Peter. (2012). Commentary on "The Optimality of Jeffreys Prior for Online Density Estimation and the Asymptotic Normality of Maximum Likelihood Estimators".. Conference on Learning Theory. 1 indexed citations
8.
Grünwald, Peter, et al.. (2011). The More We Use It the More We Love It: An Annual Survey of Teachers Suggests That Their Use of Technology Has Steadily Increased over the Years, along with the Value They Place on It. T.H.E. Journal Technological Horizons in Education. 38(6). 43. 1 indexed citations
9.
Erven, Tim van, Wouter M. Koolen, Steven de Rooij, & Peter Grünwald. (2011). Adaptive Hedge. neural information processing systems. 24. 1656–1664. 8 indexed citations
10.
Grünwald, Peter. (2011). Safe Learning: bridging the gap between Bayes, MDL and statistical learning theory via empirical convexity. Data Archiving and Networked Services (DANS). 397–420. 10 indexed citations
11.
Kotłowski, Wojciech, Peter Grünwald, & Steven de Rooij. (2010). Following the Flattened Leader. Data Archiving and Networked Services (DANS). 106–118. 4 indexed citations
12.
Grünwald, Peter. (2007). The Minimum Description Length Principle (Adaptive Computation and Machine Learning). The MIT Press eBooks. 10(1). 74–90. 102 indexed citations
13.
Erven, Tim van, Steven de Rooij, & Peter Grünwald. (2007). Catching Up Faster in Bayesian Model Selection and Model Averaging. UvA-DARE (University of Amsterdam). 20. 417–424. 8 indexed citations
14.
Gill, Richard D., et al.. (2005). The statistical strength of nonlocality proofs. TU/e Research Portal. 44 indexed citations
15.
Roos, Teemu, Peter Grünwald, Petri Myllymäki, & Kirsi Tirri. (2005). Generalization to Unseen Cases. Data Archiving and Networked Services (DANS). 18. 1129–1136. 3 indexed citations
16.
Grünwald, Peter & Joseph Y. Halpern. (2004). When ignorance is bliss. Uncertainty in Artificial Intelligence. 226–234. 20 indexed citations
17.
Grünwald, Peter & Joseph Y. Halpern. (2003). Updating Probabilities. Journal of Artificial Intelligence Research. 19(1). 243–278. 37 indexed citations
18.
Grünwald, Peter, et al.. (2003). When discriminative learning of Bayesian network parameters is easy. International Joint Conference on Artificial Intelligence. 491–496. 9 indexed citations
19.
Grünwald, Peter. (2001). Strong entropy concentration, coding, game theory and randomness. TU/e Research Portal (Eindhoven University of Technology). 2001010. 1 indexed citations
20.
Grünwald, Peter. (1990). The New Generation of Information Systems.. Phi Delta Kappan. 72(2). 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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