Michael Riis Andersen
Impact in
Papers in
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- Gaussian Processes and Bayesian Inference 11
- Topic Modeling 2
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- Sparse and Compressive Sensing Techniques 4
- Co-authors
- Lars Kai Hansen (5 shared papers)Ole Winther (3 shared papers)Aki Vehtari (6 shared papers)Martin Johansson (1 shared paper)Ib Chorkendorff (1 shared paper)Arno Solin (2 shared papers)Paul‐Christian Bürkner (1 shared paper)Gabriel Riutort‐Mayol (1 shared paper)
In The Last Decade
Michael Riis Andersen
15 papers receiving 131 citations
Peers
Comparison fields: 5 of 68
- Acoustics and Ultrasonics 2
- Computational Mathematics 1
- Signal Processing 15
- Catalysis 8
- Statistics and Probability 9
Countries citing papers authored by Michael Riis Andersen
This map shows the geographic impact of Michael Riis Andersen'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 Michael Riis Andersen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Riis Andersen more than expected).
Fields of papers citing papers by Michael Riis Andersen
This network shows the impact of papers produced by Michael Riis Andersen. 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 Michael Riis Andersen. The network helps show where Michael Riis Andersen may publish in the future.
Co-authors
The 25 scholars most cited alongside Michael Riis Andersen, 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 | Bayesian Inference for Structured Spike and Slab Priors | 2014 | 26 |
| 2 | 2022 | 26 | |
| 3 | 2005 | 25 | |
| 4 | Bayesian Inference for Spatio-temporal Spike-and-Slab Priors | 2017 | 17 |
| 5 | 2018 | 6 | |
| 6 | 2024 | 6 | |
| 7 | 2018 | 6 | |
| 8 | 2021 | 5 | |
| 9 | Bayesian structure learning for dynamic brain connectivity | 2018 | 4 |
| 10 | 2021 | 3 | |
| 11 | Sparse inference using approximate message passing | 2014 | 3 |
| 12 | 2019 | 3 | |
| 13 | Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data | 2020 | 2 |
| 14 | 2023 | 2 | |
| 15 | 2013 | 1 | |
| 16 | 2024 | 1 | |
| 17 | 2020 | 0 | |
| 18 | 2023 | 0 | |
| 19 | 2020 | 0 |
About Michael Riis Andersen
Michael Riis Andersen is a scholar working on Artificial Intelligence, Computational Mechanics, Statistics and Probability, Computational Theory and Mathematics and Control and Systems Engineering, having authored 19 papers that have together received 136 indexed citations. Recurring topics across this work include Gaussian Processes and Bayesian Inference (11 papers), Sparse and Compressive Sensing Techniques (4 papers), Statistical Methods and Inference (3 papers), Advanced Multi-Objective Optimization Algorithms (3 papers), Control Systems and Identification (2 papers), Statistical Methods and Bayesian Inference (2 papers), Topic Modeling (2 papers) and Advanced Bandit Algorithms Research (2 papers). The work is most often cited by research in Acoustics and Ultrasonics (2 citations), Computational Mathematics (1 citation), Signal Processing (15 citations), Catalysis (8 citations) and Statistics and Probability (9 citations). Michael Riis Andersen has collaborated with scholars based in Denmark, Finland and Germany. Frequent co-authors include Lars Kai Hansen, Ole Winther, Aki Vehtari, Martin Johansson, Ib Chorkendorff, Arno Solin, Paul‐Christian Bürkner, Gabriel Riutort‐Mayol, Måns Magnusson and Johan Jonasson. Their work appears in journals such as The Journal of Physical Chemistry B, Heliyon, Journal of Machine Learning Research, China Communications and Computers and Education Artificial Intelligence.
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.