Fernando V. Bonassi
Impact in
- Statistics and Probability top 5%
- Markov Chains and Monte Carlo Methods
- Statistical Methods and Inference
- Statistical Methods and Bayesian Inference
- Artificial Intelligence top 10%
- Bayesian Methods and Mixture Models
- Gaussian Processes and Bayesian Inference
- Target Tracking and Data Fusion in Sensor Networks
- Bayesian Modeling and Causal Inference
- Machine Learning and Algorithms
Papers in
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- Gaussian Processes and Bayesian Inference 1
- Bayesian Modeling and Causal Inference 1
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- Game Theory and Voting Systems 1
- Co-authors
- Robert E. McCulloch (1 shared paper)Steven L. Scott (1 shared paper)Edward I. George (1 shared paper)Alexander W. Blocker (1 shared paper)Hugh Chipman (1 shared paper)Lingchong You (1 shared paper)Mike West (1 shared paper)Raphael Nishimura (1 shared paper)
- Journals
- International Journal of Management Science and Engineering Management (1 paper)Statistical Applications in Genetics and Molecular Biology (1 paper)AIP conference proceedings (2 papers)
- Partner nations
- United StatesBrazilCanada
In The Last Decade
Fernando V. Bonassi
4 papers receiving 162 citations
Peers
Comparison fields: 5 of 60
- Statistics and Probability 83
- Artificial Intelligence 109
- Statistics, Probability and Uncertainty 12
- Computational Mathematics 1
- Management Science and Operations Research 11
Countries citing papers authored by Fernando V. Bonassi
This map shows the geographic impact of Fernando V. Bonassi'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 Fernando V. Bonassi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fernando V. Bonassi more than expected).
Fields of papers citing papers by Fernando V. Bonassi
This network shows the impact of papers produced by Fernando V. Bonassi. 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 Fernando V. Bonassi. The network helps show where Fernando V. Bonassi may publish in the future.
Co-authors
The 12 scholars most cited alongside Fernando V. Bonassi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
About Fernando V. Bonassi
Fernando V. Bonassi is a scholar working on Artificial Intelligence, Economics and Econometrics, Molecular Biology, Management Science and Operations Research and Statistics and Probability, having authored 4 papers that have together received 171 indexed citations. Recurring topics across this work include Game Theory and Voting Systems (1 paper), Gene Regulatory Network Analysis (1 paper), Markov Chains and Monte Carlo Methods (1 paper), Gaussian Processes and Bayesian Inference (1 paper), Bayesian Modeling and Causal Inference (1 paper), Statistical Methods and Inference (1 paper), Bioinformatics and Genomic Networks (1 paper) and Game Theory and Applications (1 paper). The work is most often cited by research in Statistics and Probability (83 citations), Artificial Intelligence (109 citations), Statistics, Probability and Uncertainty (12 citations), Computational Mathematics (1 citation) and Management Science and Operations Research (11 citations). Fernando V. Bonassi has collaborated with scholars based in United States, Brazil and Canada. Frequent co-authors include Robert E. McCulloch, Steven L. Scott, Edward I. George, Alexander W. Blocker, Hugh Chipman, Lingchong You, Mike West, Raphael Nishimura, Paul M. Goggans and Carlos Alberto de Bragança Pereira. Their work appears in journals such as International Journal of Management Science and Engineering Management, Statistical Applications in Genetics and Molecular Biology and AIP conference proceedings.
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.