Guido Consonni
- Statistics and Probability top 1%
- Artificial Intelligence top 5%
- Molecular Biology
- Management Science and Operations Research top 10%
- Statistics, Probability and Uncertainty top 5%
- Co-authors
- Jean‐Michel MarinDimitris FouskakisBrunero LiseoIoannis NtzoufrasStefano PelusoEduardo Gutiérrez‐PeñaΠέτρος ΔελλαπόρταςLeonardo Bottolo
- Topics
- Bayesian Modeling and Causal Inference (25 papers)Statistical Methods and Bayesian Inference (23 papers)Bayesian Methods and Mixture Models (19 papers)
- Partner nations
- ItalyGreeceUnited Kingdom
In The Last Decade
Guido Consonni
50 papers receiving 536 citations
Peers
Comparison fields: 5 of 95
- Statistics and Probability 357
- Artificial Intelligence 287
- Molecular Biology 69
- Management Science and Operations Research 48
- Statistics, Probability and Uncertainty 42
Countries citing papers authored by Guido Consonni
This map shows the geographic impact of Guido Consonni'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 Guido Consonni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guido Consonni more than expected).
Fields of papers citing papers by Guido Consonni
This network shows the impact of papers produced by Guido Consonni. 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 Guido Consonni. The network helps show where Guido Consonni may publish in the future.
Co-authorship network of co-authors of Guido Consonni
This figure shows the co-authorship network connecting the top 25 collaborators of Guido Consonni. A scholar is included among the top collaborators of Guido Consonni 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 Guido Consonni. Guido Consonni is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 6 | |
| 3 | 3 | |
| 4 | 6 | |
| 5 | 1 | |
| 6 | 3 | |
| 7 | 3 | |
| 8 | 11 | |
| 9 | 9 | |
| 10 | Objective Bayesian Search of Gaussian DAG Models with Non-local Priors | 0 |
| 11 | 1 | |
| 12 | Gibbs Sampling, Exponential Families and Orthogonal Polynomials. Comment. | 1 |
| 13 | 8 | |
| 14 | 3 | |
| 15 | 2 | |
| 16 | Relaxing the local independence assumption for quantitative learning in acyclic directed graphical models through hierarchical partition models. | 6 |
| 17 | 2 | |
| 18 | 8 | |
| 19 | 30 | |
| 20 | 7 |
About Guido Consonni
Guido Consonni is a scholar working on Statistics and Probability, Artificial Intelligence and Signal Processing, having authored 53 papers that have together received 567 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (25 papers), Statistical Methods and Bayesian Inference (23 papers) and Bayesian Methods and Mixture Models (19 papers). The work is most often cited by research in Statistics and Probability (357 citations), Artificial Intelligence (287 citations) and Statistics, Probability and Uncertainty (42 citations). Guido Consonni has collaborated with scholars based in Italy, Greece and United Kingdom. Frequent co-authors include Jean‐Michel Marin, Dimitris Fouskakis, Brunero Liseo, Ioannis Ntzoufras, Stefano Peluso, Eduardo Gutiérrez‐Peña, Πέτρος Δελλαπόρτας, Leonardo Bottolo, Antonio Lijoi and Alberto Roverato. Their work appears in journals such as Journal of the American Statistical Association, Biometrics and Biometrika.
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.