Jesse Windle
Impact in
- Statistics and Probability top 2%
- Statistical Methods and Inference
- Statistical Methods and Bayesian Inference
- Advanced Statistical Methods and Models
- Markov Chains and Monte Carlo Methods
- Computational Mathematics top 10%
Papers in
-
- Bayesian Methods and Mixture Models 4
-
- Statistical Methods and Bayesian Inference 3
- Statistical Methods and Inference 2
- Co-authors
- James G. Scott (3 shared papers)Nicholas G. Polson (2 shared papers)Carlos M. Carvalho (2 shared papers)Julia K. Olsen (1 shared paper)Julia Olsen (1 shared paper)L. Sun (1 shared paper)
- Journals
- Bayesian Analysis (1 paper)Journal of the Royal Statistical Society Series B (Statistical Methodology) (1 paper)Journal of the American Statistical Association (1 paper)College Mathematics Journal (2 papers)arXiv (Cornell University) (1 paper)
- Partner nations
- United States
In The Last Decade
Jesse Windle
6 papers receiving 592 citations
Jesse Windle's Hit Papers
Peers
Comparison fields: 5 of 106
- Statistics and Probability 274
- Computational Mathematics 11
- Artificial Intelligence 274
- Ecological Modeling 18
- Management Science and Operations Research 38
Countries citing papers authored by Jesse Windle
This map shows the geographic impact of Jesse Windle'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 Jesse Windle with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jesse Windle more than expected).
Fields of papers citing papers by Jesse Windle
This network shows the impact of papers produced by Jesse Windle. 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 Jesse Windle. The network helps show where Jesse Windle may publish in the future.
Co-authors
The 6 scholars most cited alongside Jesse Windle, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables Hit paper breakdown → | 2013 | 519 |
| 2 | 2013 | 66 | |
| 3 | 2014 | 13 | |
| 4 | 2005 | 9 | |
| 5 | 2005 | 3 | |
| 6 | Polya-Gamma Data Augmentation for Dynamic Models | 2013 | 1 |
About Jesse Windle
Jesse Windle is a scholar working on Artificial Intelligence, Statistics and Probability, Information Systems, Molecular Biology and Sociology and Political Science, having authored 6 papers that have together received 611 indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (4 papers), Statistical Methods and Bayesian Inference (3 papers), Statistical Methods and Inference (2 papers), User Authentication and Security Systems (2 papers), Handwritten Text Recognition Techniques (1 paper), Advanced Steganography and Watermarking Techniques (1 paper), Gene expression and cancer classification (1 paper) and Financial Risk and Volatility Modeling (1 paper). The work is most often cited by research in Statistics and Probability (274 citations), Computational Mathematics (11 citations), Artificial Intelligence (274 citations), Ecological Modeling (18 citations) and Management Science and Operations Research (38 citations). Jesse Windle has collaborated with scholars based in United States. Frequent co-authors include James G. Scott, Nicholas G. Polson, Carlos M. Carvalho, Julia K. Olsen, Julia Olsen and L. Sun. Their work appears in journals such as Bayesian Analysis, Journal of the Royal Statistical Society Series B (Statistical Methodology), Journal of the American Statistical Association, College Mathematics Journal and arXiv (Cornell University).
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.