Thiago G. Martins
- Statistics and Probability top 2%
- Economics and Econometrics top 5%
- Global and Planetary Change top 10%
- Ecology top 10%
- Artificial Intelligence top 10%
- Co-authors
- Håvard RueDaniel SimpsonSigrunn H. SørbyeAndrea RieblerFinn LindgrenLeonhard HeldMałgorzata RoosMarcos O. Prates
- Topics
- Statistical Methods and Bayesian Inference (5 papers)Bayesian Methods and Mixture Models (4 papers)Gaussian Processes and Bayesian Inference (3 papers)
- Journals
- Computational Statistics & Data AnalysisStatistical ScienceScandinavian Journal of Statistics
- Partner nations
- NorwayBrazilSwitzerland
In The Last Decade
Thiago G. Martins
9 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 136
- Statistics and Probability 233
- Economics and Econometrics 187
- Global and Planetary Change 176
- Ecology 153
- Artificial Intelligence 139
Countries citing papers authored by Thiago G. Martins
This map shows the geographic impact of Thiago G. Martins'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 Thiago G. Martins with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thiago G. Martins more than expected).
Fields of papers citing papers by Thiago G. Martins
This network shows the impact of papers produced by Thiago G. Martins. 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 Thiago G. Martins. The network helps show where Thiago G. Martins may publish in the future.
Co-authorship network of co-authors of Thiago G. Martins
This figure shows the co-authorship network connecting the top 25 collaborators of Thiago G. Martins. A scholar is included among the top collaborators of Thiago G. Martins 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 Thiago G. Martins. Thiago G. Martins is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priorsbreakdown → | 622 |
| 3 | 2 | |
| 4 | 11 | |
| 5 | Sensitivity analysis for Bayesian hierarchical models | 46 |
| 6 | Bayesian computing with INLA: New featuresbreakdown → | 402 |
| 7 | 1 | |
| 8 | 3 | |
| 9 | 1 |
About Thiago G. Martins
Thiago G. Martins is a scholar working on Statistics and Probability, Geography, Planning and Development and Artificial Intelligence, having authored 9 papers that have together received 1.1k indexed citations. Recurring topics across this work include Statistical Methods and Bayesian Inference (5 papers), Bayesian Methods and Mixture Models (4 papers) and Gaussian Processes and Bayesian Inference (3 papers). The work is most often cited by research in Statistics and Probability (233 citations), Modeling and Simulation (89 citations) and Ecological Modeling (81 citations). Thiago G. Martins has collaborated with scholars based in Norway, Brazil and Switzerland. Frequent co-authors include Håvard Rue, Daniel Simpson, Sigrunn H. Sørbye, Andrea Riebler, Finn Lindgren, Leonhard Held, Małgorzata Roos, Marcos O. Prates, Edna Afonso Reis and Dani Gamerman. Their work appears in journals such as Computational Statistics & Data Analysis, Statistical Science and Scandinavian Journal of Statistics.
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.