Paola Vicard

29 papers receiving 228 citations

Peers

Paola Vicard
Comparison fields: 5 of 69
  • Artificial Intelligence 106
  • Genetics 65
  • Management Science and Operations Research 52
  • Statistics and Probability 39
  • Molecular Biology 31
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Yuexiao Dong United States
Wei Lan China
Nicolas de Condorcet
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Citations per field
00.5×4.6×
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Citations per year

Countries citing papers authored by Paola Vicard

Since Specialization
Citations

This map shows the geographic impact of Paola Vicard'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 Paola Vicard with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Paola Vicard more than expected).

Fields of papers citing papers by Paola Vicard

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Paola Vicard. 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 Paola Vicard. The network helps show where Paola Vicard may publish in the future.

Co-authorship network of co-authors of Paola Vicard

This figure shows the co-authorship network connecting the top 25 collaborators of Paola Vicard. A scholar is included among the top collaborators of Paola Vicard 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 Paola Vicard. Paola Vicard is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 2
2 8
3 5
4 2
5 2
6
Network modeling the bike-sharing intention: an empirical analysis of non user needings
1
7 3
8 12
9 19
10
Monitoring and Improving Greek Banking Services Using Bayesian Networks: an Analysis of Mystery Shopping Data
1
11 19
12
Un modello di valutazione della qualità basato su sistemi esperti probabilistici
2
13 51
14
Multivariate techniques for imputation based on Bayesian networks
11
15 2
16 17
17
Estimation of mutation rates from paternity cases using a Bayesian network
2
18
New statistical approaches for estimating mutation parameters
1
19
A proposal for the use of graphical representation in official statistics
1
20 14

About Paola Vicard

Paola Vicard is a scholar working on Statistics and Probability, Artificial Intelligence and Management Science and Operations Research, having authored 31 papers that have together received 241 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (16 papers), Statistical Methods and Bayesian Inference (8 papers) and Bayesian Methods and Mixture Models (4 papers). The work is most often cited by research in Statistics and Probability (39 citations), Management Science and Operations Research (52 citations) and Artificial Intelligence (106 citations). Paola Vicard has collaborated with scholars based in Italy, United Kingdom and Greece. Frequent co-authors include Julia Mortera, A. P. Dawid, Maria Francesca Renzi, Roberta Guglielmetti Mugion, Ioannis Ntzoufras, Laura Di Pietro, Steffen L. Lauritzen, Mauro Scanu, Giulia Sacco and Clelia Di Serio. Their work appears in journals such as Expert Systems with Applications, Biometrika and Transportation Research Part A Policy and Practice.

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.

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