Scott A. Sisson
- Statistics and Probability top 0.5%
- Statistical Methods and Inference 19
- Markov Chains and Monte Carlo Methods 16
- Statistical Methods and Bayesian Inference 13
- Global and Planetary Change top 1%
- Hydrology and Drought Analysis 12
- Climate variability and models 11
- Water Science and Technology top 2%
- Atmospheric Science top 5%
- Artificial Intelligence top 1%
- Bayesian Methods and Mixture Models 22
- Gaussian Processes and Bayesian Inference 15
- Bayesian Modeling and Causal Inference 10
Scott A. Sisson
104 papers receiving 3.6k citations
Hit Papers
Peers
Comparison fields: 5 of 167
- Statistics and Probability 938
- Global and Planetary Change 1.2k
- Water Science and Technology 433
- Atmospheric Science 533
- Artificial Intelligence 870
Countries citing papers authored by Scott A. Sisson
This map shows the geographic impact of Scott A. Sisson'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 Scott A. Sisson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Scott A. Sisson more than expected).
Fields of papers citing papers by Scott A. Sisson
This network shows the impact of papers produced by Scott A. Sisson. 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 Scott A. Sisson. The network helps show where Scott A. Sisson may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Scott A. Sisson, 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 | 2025 | 1 | |
| 2 | 2022 | 0 | |
| 3 | 2022 | 9 | |
| 4 | 2022 | 5 | |
| 5 | 2021 | 16 | |
| 6 | 2021 | 9 | |
| 7 | Continuous-time edge modelling using non-parametric point processes | 2021 | 1 |
| 8 | 2020 | 91 | |
| 9 | 2020 | 11 | |
| 10 | 2020 | 13 | |
| 11 | 2019 | 13 | |
| 12 | Binary Space Partitioning Forest | 2019 | 2 |
| 13 | 2019 | 11 | |
| 14 | 2018 | 6 | |
| 15 | On some variance reduction properties of the reparameterization trick. | 2018 | 1 |
| 16 | 2017 | 17 | |
| 17 | 2016 | 25 | |
| 18 | 2015 | 20 | |
| 19 | 2009 | 27 | |
| 20 | 2006 | 18 |
About Scott A. Sisson
Scott A. Sisson is a scholar working on Statistics and Probability, Artificial Intelligence, Global and Planetary Change, Ecological Modeling and Finance, having authored 107 papers that have together received 3.8k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (22 papers), Statistical Methods and Inference (19 papers), Markov Chains and Monte Carlo Methods (16 papers), Gaussian Processes and Bayesian Inference (15 papers), Statistical Methods and Bayesian Inference (13 papers), Hydrology and Drought Analysis (12 papers), Climate variability and models (11 papers) and Bayesian Modeling and Causal Inference (10 papers). The work is most often cited by research in Statistics and Probability (938 citations), Global and Planetary Change (1.2k citations), Water Science and Technology (433 citations), Atmospheric Science (533 citations) and Artificial Intelligence (870 citations). Scott A. Sisson has collaborated with scholars based in Australia, United States and China. Frequent co-authors include Yanan Fan, Mark M. Tanaka, Seth Westra, Stuart Coles, Feifei Zheng, Simone A. Padoan, Luis R. Pericchi, Mathieu Ribatet, Gareth W. Peters and Ashish Sharma. Their work appears in journals such as Computational Statistics & Data Analysis, Statistics and Computing, Water Research, Water Resources Research and Journal of Computational and Graphical Statistics.
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.