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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- Markov Chains and Monte Carlo Methods 1
- Statistical Methods and Inference 1
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- Game Theory and Applications 1
- Co-authors
- Hugh ChipmanAlexander W. BlockerRobert E. McCullochSteven L. ScottEdward I. GeorgeLingchong YouMike WestPaul M. Goggans
- Journals
- Statistical Applications in Genetics and Molecular Biology (1 paper)International Journal of Management Science and Engineering Management (1 paper)AIP conference proceedings (2 papers)
- Partner nations
- United StatesBrazilCanada
In The Last Decade
Fernando V. Bonassi
4 papers receiving 160 citations
Peers
Comparison fields: 5 of 60
- Statistics and Probability 81
- Artificial Intelligence 107
- Computational Mathematics 1
- Statistics, Probability and Uncertainty 12
- 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-authorship network
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 Statistics and Probability, Management Science and Operations Research, Economics and Econometrics, Artificial Intelligence and Molecular Biology, having authored 4 papers that have together received 169 indexed citations. Recurring topics across this work include Markov Chains and Monte Carlo Methods (1 paper), Statistical Methods and Inference (1 paper), Single-cell and spatial transcriptomics (1 paper), Bayesian Modeling and Causal Inference (1 paper), Bioinformatics and Genomic Networks (1 paper), Game Theory and Applications (1 paper), Game Theory and Voting Systems (1 paper) and Gene Regulatory Network Analysis (1 paper). The work is most often cited by research in Statistics and Probability (81 citations), Artificial Intelligence (107 citations), Computational Mathematics (1 citation), Statistics, Probability and Uncertainty (12 citations) 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 Hugh Chipman, Alexander W. Blocker, Robert E. McCulloch, Steven L. Scott, Edward I. George, Lingchong You, Mike West, Paul M. Goggans, Raphael Nishimura and Carlos Alberto de Bragança Pereira. Their work appears in journals such as Statistical Applications in Genetics and Molecular Biology, International Journal of Management Science and Engineering Management 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.