Timo Koski
Impact in
- Statistics and Probability top 5%
- Statistical Methods and Inference
- Artificial Intelligence top 5%
- Bayesian Modeling and Causal Inference
- Bayesian Methods and Mixture Models
- Algorithms and Data Compression
- Machine Learning and Algorithms
Papers in
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- Bayesian Methods and Mixture Models 18
- Bayesian Modeling and Causal Inference 13
- Neural Networks and Applications 5
- Machine Learning and Algorithms 5
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- Genomics and Phylogenetic Studies 9
- Co-authors
- John M. Noble (2 shared papers)Mats Gyllenberg (34 shared papers)Jukka Corander (15 shared papers)Martin Verlaan (5 shared papers)Johan Pensar (3 shared papers)Bertrand Séraphin (1 shared paper)Óscar Puig (1 shared paper)Elisabeth Bragado‐Nilsson (1 shared paper)
In The Last Decade
Timo Koski
75 papers receiving 933 citations
Peers
Comparison fields: 5 of 136
- Statistics and Probability 129
- Artificial Intelligence 466
- Signal Processing 126
- Statistics, Probability and Uncertainty 46
- Management Science and Operations Research 59
Countries citing papers authored by Timo Koski
This map shows the geographic impact of Timo Koski'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 Timo Koski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Timo Koski more than expected).
Fields of papers citing papers by Timo Koski
This network shows the impact of papers produced by Timo Koski. 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 Timo Koski. The network helps show where Timo Koski may publish in the future.
Co-authors
The 25 scholars most cited alongside Timo Koski, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 79 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Bayesian Networks: An Introduction | 2009 | 138 |
| 2 | 2001 | 134 | |
| 3 | 2009 | 79 | |
| 4 | 2006 | 47 | |
| 5 | 2007 | 42 | |
| 6 | 1997 | 35 | |
| 7 | 2008 | 33 | |
| 8 | 2006 | 32 | |
| 9 | 1994 | 26 | |
| 10 | 2014 | 25 | |
| 11 | 2001 | 23 | |
| 12 | 1994 | 23 | |
| 13 | 2009 | 18 | |
| 14 | 2013 | 17 | |
| 15 | 1992 | 15 | |
| 16 | 1986 | 15 | |
| 17 | 1997 | 15 | |
| 18 | 1994 | 14 | |
| 19 | 2014 | 13 | |
| 20 | 2014 | 13 |
About Timo Koski
Timo Koski is a scholar working on Artificial Intelligence, Molecular Biology, Statistics and Probability, Signal Processing and Genetics, having authored 79 papers that have together received 1.0k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (18 papers), Bayesian Modeling and Causal Inference (13 papers), Genomics and Phylogenetic Studies (9 papers), Control Systems and Identification (5 papers), Statistical Mechanics and Entropy (5 papers), Neural Networks and Applications (5 papers), Machine Learning and Algorithms (5 papers) and Blind Source Separation Techniques (5 papers). The work is most often cited by research in Statistics and Probability (129 citations), Artificial Intelligence (466 citations), Signal Processing (126 citations), Statistics, Probability and Uncertainty (46 citations) and Management Science and Operations Research (59 citations). Timo Koski has collaborated with scholars based in Sweden, Finland and Czechia. Frequent co-authors include John M. Noble, Mats Gyllenberg, Jukka Corander, Martin Verlaan, Johan Pensar, Bertrand Séraphin, Óscar Puig, Elisabeth Bragado‐Nilsson, Helge Gyllenberg and Mark S. Johnson. Their work appears in journals such as IEEE Transactions on Information Theory, Information Sciences, International Statistical Review, Data Mining and Knowledge Discovery and Statistics and Computing.
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.