Joris Mulder
- Statistics and Probability top 0.5%
- Experimental and Cognitive Psychology top 2%
- Management Science and Operations Research top 2%
- Artificial Intelligence top 5%
- Cognitive Neuroscience top 10%
- Co-authors
- Herbert HoijtinkXin GuSara van ErpDaniel L. OberskiDonald R. WilliamsWim J. van der LindenRoger LeendersCaspar J. Van Lissa
- Topics
- Statistical Methods and Bayesian Inference (33 papers)Advanced Statistical Methods and Models (23 papers)Mental Health Research Topics (12 papers)
- Cited by
- Statistics and ProbabilityExperimental and Cognitive PsychologyManagement Science and Operations Research
- Partner nations
- NetherlandsUnited StatesChina
In The Last Decade
Joris Mulder
73 papers receiving 1.7k citations
Peers
Comparison fields: 5 of 150
- Statistics and Probability 589
- Experimental and Cognitive Psychology 403
- Management Science and Operations Research 285
- Artificial Intelligence 257
- Cognitive Neuroscience 233
Countries citing papers authored by Joris Mulder
This map shows the geographic impact of Joris Mulder'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 Joris Mulder with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joris Mulder more than expected).
Fields of papers citing papers by Joris Mulder
This network shows the impact of papers produced by Joris Mulder. 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 Joris Mulder. The network helps show where Joris Mulder may publish in the future.
Co-authorship network of co-authors of Joris Mulder
This figure shows the co-authorship network connecting the top 25 collaborators of Joris Mulder. A scholar is included among the top collaborators of Joris Mulder 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 Joris Mulder. Joris Mulder is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 3 | |
| 4 | 8 | |
| 5 | 6 | |
| 6 | 1 | |
| 7 | 6 | |
| 8 | Bayesian analysis of higher-order network autocorrelation models | 0 |
| 9 | 6 | |
| 10 | 51 | |
| 11 | 139 | |
| 12 | 7 | |
| 13 | 27 | |
| 14 | 17 | |
| 15 | 10 | |
| 16 | 83 | |
| 17 | 22 | |
| 18 | 19 | |
| 19 | 37 | |
| 20 | Bayesian Network Models for Local Dependence among Observable Outcome Variables. Research Report. ETS RR-06-36. | 3 |
About Joris Mulder
Joris Mulder is a scholar working on Statistics and Probability, Management Science and Operations Research and Statistical and Nonlinear Physics, having authored 79 papers that have together received 1.7k indexed citations. Recurring topics across this work include Statistical Methods and Bayesian Inference (33 papers), Advanced Statistical Methods and Models (23 papers) and Mental Health Research Topics (12 papers). The work is most often cited by research in Statistics and Probability (589 citations), Experimental and Cognitive Psychology (403 citations) and Management Science and Operations Research (285 citations). Joris Mulder has collaborated with scholars based in Netherlands, United States and China. Frequent co-authors include Herbert Hoijtink, Xin Gu, Sara van Erp, Daniel L. Oberski, Donald R. Williams, Wim J. van der Linden, Roger Leenders, Caspar J. Van Lissa, Irene Klugkist and Eric‐Jan Wagenmakers. Their work appears in journals such as PLoS ONE, Journal of Management and Developmental Psychology.
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.