Ritabrata Dutta
- Artificial Intelligence top 10%
- Mechanical Engineering
- Statistics and Probability top 5%
- Biomedical Engineering
- Ecology
- Co-authors
- Samuel KaskiMichael U. GutmannJukka CoranderAlfred SteinAntonietta MiraS. PaulTapas Kumar DuttaArnab Atta
- Topics
- Markov Chains and Monte Carlo Methods (9 papers)Gaussian Processes and Bayesian Inference (5 papers)Bayesian Methods and Mixture Models (3 papers)
- Partner nations
- United KingdomIndiaSwitzerland
In The Last Decade
Ritabrata Dutta
31 papers receiving 483 citations
Peers
Comparison fields: 5 of 113
- Artificial Intelligence 135
- Mechanical Engineering 93
- Statistics and Probability 90
- Biomedical Engineering 63
- Ecology 47
Countries citing papers authored by Ritabrata Dutta
This map shows the geographic impact of Ritabrata Dutta'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 Ritabrata Dutta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ritabrata Dutta more than expected).
Fields of papers citing papers by Ritabrata Dutta
This network shows the impact of papers produced by Ritabrata Dutta. 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 Ritabrata Dutta. The network helps show where Ritabrata Dutta may publish in the future.
Co-authorship network of co-authors of Ritabrata Dutta
This figure shows the co-authorship network connecting the top 25 collaborators of Ritabrata Dutta. A scholar is included among the top collaborators of Ritabrata Dutta 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 Ritabrata Dutta. Ritabrata Dutta 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 | 2 | |
| 3 | 3 | |
| 4 | 2 | |
| 5 | 17 | |
| 6 | 37 | |
| 7 | Likelihood-free inference by penalised logistic regression | 4 |
| 8 | 8 | |
| 9 | 105 | |
| 10 | 5 | |
| 11 | 31 | |
| 12 | 6 | |
| 13 | 26 | |
| 14 | 45 | |
| 15 | Delineation of diseased tea patches using MXL and texture based classification | 9 |
| 16 | 37 | |
| 17 | 12 | |
| 18 | 25 | |
| 19 | 10 | |
| 20 | 13 |
About Ritabrata Dutta
Ritabrata Dutta is a scholar working on Statistics and Probability, General Social Sciences and Statistics, Probability and Uncertainty, having authored 31 papers that have together received 511 indexed citations. Recurring topics across this work include Markov Chains and Monte Carlo Methods (9 papers), Gaussian Processes and Bayesian Inference (5 papers) and Bayesian Methods and Mixture Models (3 papers). The work is most often cited by research in Statistics and Probability (90 citations), Modeling and Simulation (20 citations) and Artificial Intelligence (135 citations). Ritabrata Dutta has collaborated with scholars based in United Kingdom, India and Switzerland. Frequent co-authors include Samuel Kaski, Michael U. Gutmann, Jukka Corander, Alfred Stein, Antonietta Mira, S. Paul, Tapas Kumar Dutta, Arnab Atta, Angshuman Chattopadhyay and R.M. Bhagat. Their work appears in journals such as The Journal of Chemical Physics, Bioinformatics and Food Chemistry.
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.