Mauro Piccioni
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Statistics and Probability top 5%
- Mathematical Physics top 10%
- Computational Theory and Mathematics top 10%
- Co-authors
- Yali AmitUlf GrenanderAlfredo GermaniArnoldo FrigessiPiero BaroneNeal MadrasSergio ScarlattiJohn H. Elton
- Topics
- Markov Chains and Monte Carlo Methods (11 papers)Bayesian Methods and Mixture Models (8 papers)Statistical Methods and Inference (8 papers)
- Journals
- Journal of the American Statistical AssociationIEEE Transactions on Information TheoryJournal of the Franklin Institute
- Partner nations
- ItalyFranceUnited States
In The Last Decade
Mauro Piccioni
40 papers receiving 512 citations
Peers
Comparison fields: 5 of 72
- Computer Vision and Pattern Recognition 203
- Artificial Intelligence 180
- Statistics and Probability 123
- Mathematical Physics 82
- Computational Theory and Mathematics 68
Countries citing papers authored by Mauro Piccioni
This map shows the geographic impact of Mauro Piccioni'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 Mauro Piccioni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mauro Piccioni more than expected).
Fields of papers citing papers by Mauro Piccioni
This network shows the impact of papers produced by Mauro Piccioni. 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 Mauro Piccioni. The network helps show where Mauro Piccioni may publish in the future.
Co-authorship network of co-authors of Mauro Piccioni
This figure shows the co-authorship network connecting the top 25 collaborators of Mauro Piccioni. A scholar is included among the top collaborators of Mauro Piccioni 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 Mauro Piccioni. Mauro Piccioni is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 4 | |
| 3 | 1 | |
| 4 | 7 | |
| 5 | 5 | |
| 6 | 2 | |
| 7 | 1 | |
| 8 | A note on the IPF algorithm when the marginal problem is unsolvable | 3 |
| 9 | 3 | |
| 10 | 23 | |
| 11 | 4 | |
| 12 | 10 | |
| 13 | 10 | |
| 14 | 206 | |
| 15 | 1 | |
| 16 | 21 | |
| 17 | 1 | |
| 18 | 1 | |
| 19 | 0 | |
| 20 | 1 |
About Mauro Piccioni
Mauro Piccioni is a scholar working on Statistics and Probability, Mathematical Physics and Finance, having authored 41 papers that have together received 583 indexed citations. Recurring topics across this work include Markov Chains and Monte Carlo Methods (11 papers), Bayesian Methods and Mixture Models (8 papers) and Statistical Methods and Inference (8 papers). The work is most often cited by research in Statistics and Probability (123 citations), Computer Vision and Pattern Recognition (203 citations) and Mathematical Physics (82 citations). Mauro Piccioni has collaborated with scholars based in Italy, France and United States. Frequent co-authors include Yali Amit, Ulf Grenander, Alfredo Germani, Arnoldo Frigessi, Piero Barone, Neal Madras, Sergio Scarlatti, John H. Elton, L. Jetto and Paolo Baldi. Their work appears in journals such as Journal of the American Statistical Association, IEEE Transactions on Information Theory and Journal of the Franklin Institute.
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.