Joris Mulder
- Statistics and Probability top 0.5%
- Statistical Methods and Bayesian Inference 33
- Advanced Statistical Methods and Models 23
- Statistical Methods and Inference 12
- Statistical Methods in Clinical Trials 11
-
- Mental Health Research Topics 12
- General Decision Sciences top 10%
- Applied Psychology top 10%
-
- Complex Network Analysis Techniques 11
- Opinion Dynamics and Social Influence 9
-
- Bayesian Modeling and Causal Inference 9
- Co-authors
- Herbert HoijtinkXin GuSara van ErpDaniel L. OberskiDonald R. WilliamsWim J. van der LindenRoger LeendersCaspar J. Van Lissa
- 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
- General Decision Sciences 38
- Applied Psychology 72
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
The 25 scholars most cited alongside Joris Mulder, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 0 | |
| 3 | 2023 | 3 | |
| 4 | 2023 | 8 | |
| 5 | 2023 | 6 | |
| 6 | 2022 | 1 | |
| 7 | 2021 | 6 | |
| 8 | Bayesian analysis of higher-order network autocorrelation models | 2021 | 0 |
| 9 | 2020 | 6 | |
| 10 | 2020 | 51 | |
| 11 | 2019 | 139 | |
| 12 | 2019 | 7 | |
| 13 | 2018 | 27 | |
| 14 | 2018 | 17 | |
| 15 | 2017 | 10 | |
| 16 | 2017 | 83 | |
| 17 | 2012 | 22 | |
| 18 | 2011 | 19 | |
| 19 | 2011 | 37 | |
| 20 | Bayesian Network Models for Local Dependence among Observable Outcome Variables. Research Report. ETS RR-06-36. | 2006 | 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), Mental Health Research Topics (12 papers), Statistical Methods and Inference (12 papers), Statistical Methods in Clinical Trials (11 papers), Complex Network Analysis Techniques (11 papers), Bayesian Modeling and Causal Inference (9 papers) and Opinion Dynamics and Social Influence (9 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.