Jiří Vomlel
- Artificial Intelligence top 5%
- Management Science and Operations Research top 10%
- Software top 10%
- Information Systems
- Signal Processing
- Co-authors
- Marco ValtortaYoung‐Gyun KimMilan StudenýPetr SavickýHelge LangsethFinn V. JensenRaymond HemmeckeUffe Kjærulff
- Topics
- Bayesian Modeling and Causal Inference (24 papers)AI-based Problem Solving and Planning (4 papers)Rough Sets and Fuzzy Logic (4 papers)
- Journals
- Soft ComputingInternational Journal of Approximate ReasoningInternational Journal of Intelligent Systems
- Partner nations
- CzechiaDenmarkUnited States
In The Last Decade
Jiří Vomlel
29 papers receiving 286 citations
Peers
Comparison fields: 5 of 56
- Artificial Intelligence 242
- Management Science and Operations Research 53
- Software 39
- Information Systems 37
- Signal Processing 35
Countries citing papers authored by Jiří Vomlel
This map shows the geographic impact of Jiří Vomlel'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 Jiří Vomlel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jiří Vomlel more than expected).
Fields of papers citing papers by Jiří Vomlel
This network shows the impact of papers produced by Jiří Vomlel. 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 Jiří Vomlel. The network helps show where Jiří Vomlel may publish in the future.
Co-authorship network of co-authors of Jiří Vomlel
This figure shows the co-authorship network connecting the top 25 collaborators of Jiří Vomlel. A scholar is included among the top collaborators of Jiří Vomlel 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 Jiří Vomlel. Jiří Vomlel is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 3 | |
| 3 | 2 | |
| 4 | 31 | |
| 5 | Representations of Bayesian networks by low-rank models | 1 |
| 6 | 2 | |
| 7 | 5 | |
| 8 | 2 | |
| 9 | 3 | |
| 10 | Rank of tensors of l-out-of-k functions: an application in probabilistic inference. | 1 |
| 11 | 21 | |
| 12 | 9 | |
| 13 | Exploiting tensor rank-one decomposition in probabilistic inference | 17 |
| 14 | Noisy-or classifier: Research Articles | 7 |
| 15 | 17 | |
| 16 | Building adaptive tests using Bayesian networks | 4 |
| 17 | 34 | |
| 18 | 12 | |
| 19 | 40 | |
| 20 | Troubleshooting: NP-Hardness and Solution Methods | 7 |
About Jiří Vomlel
Jiří Vomlel is a scholar working on Computational Mathematics, Artificial Intelligence and Management Science and Operations Research, having authored 30 papers that have together received 335 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (24 papers), AI-based Problem Solving and Planning (4 papers) and Rough Sets and Fuzzy Logic (4 papers). The work is most often cited by research in Computational Mathematics (7 citations), Software (39 citations) and Artificial Intelligence (242 citations). Jiří Vomlel has collaborated with scholars based in Czechia, Denmark and United States. Frequent co-authors include Marco Valtorta, Young‐Gyun Kim, Milan Studený, Petr Savický, Helge Langseth, Finn V. Jensen, Raymond Hemmecke, Uffe Kjærulff, N. Bello González and R. Schlichenmaier. Their work appears in journals such as Soft Computing, International Journal of Approximate Reasoning and International Journal of Intelligent Systems.
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.