Yuling Yao
Impact in
- Statistics and Probability top 5%
- Statistical Methods and Bayesian Inference
- Statistical Methods and Inference
Papers in
-
- Statistical Methods and Bayesian Inference 6
- Markov Chains and Monte Carlo Methods 2
-
- Gaussian Processes and Bayesian Inference 1
- Data Analysis with R 1
- Co-authors
- Andrew Gelman (7 shared papers)Aki Vehtari (4 shared papers)Daniel Simpson (1 shared paper)Jonah Gabry (2 shared papers)Måns Magnusson (1 shared paper)Topi Paananen (1 shared paper)Paul‐Christian Bürkner (2 shared papers)Alexander van Geen (2 shared papers)
- Journals
- JAMA Network Open (1 paper)Bayesian Analysis (1 paper)Computational Brain & Behavior (1 paper)Physical review. D (1 paper)Journal of Physics G Nuclear and Particle Physics (1 paper)
- Partner nations
- United StatesGermanyFinland
In The Last Decade
Yuling Yao
10 papers receiving 246 citations
Peers
Comparison fields: 5 of 84
- Statistics and Probability 57
- General Psychology 5
- Statistics, Probability and Uncertainty 14
- General Decision Sciences 5
- Astronomy and Astrophysics 30
Countries citing papers authored by Yuling Yao
This map shows the geographic impact of Yuling Yao'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 Yuling Yao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuling Yao more than expected).
Fields of papers citing papers by Yuling Yao
This network shows the impact of papers produced by Yuling Yao. 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 Yuling Yao. The network helps show where Yuling Yao may publish in the future.
Co-authors
The 25 scholars most cited alongside Yuling Yao, 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 | 2018 | 57 | |
| 2 | Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models [R package loo version 2.4.1] | 2020 | 53 |
| 3 | 2021 | 29 | |
| 4 | 2017 | 27 | |
| 5 | 2024 | 25 | |
| 6 | 2020 | 18 | |
| 7 | 2020 | 16 | |
| 8 | 2021 | 14 | |
| 9 | 2021 | 10 | |
| 10 | 2023 | 1 | |
| 11 | 2026 | 0 |
About Yuling Yao
Yuling Yao is a scholar working on Statistics and Probability, Artificial Intelligence, Modeling and Simulation, Statistics, Probability and Uncertainty and Information Systems and Management, having authored 11 papers that have together received 250 indexed citations. Recurring topics across this work include Statistical Methods and Bayesian Inference (6 papers), Markov Chains and Monte Carlo Methods (2 papers), Forecasting Techniques and Applications (1 paper), Gaussian Processes and Bayesian Inference (1 paper), Data Analysis with R (1 paper), Heavy metals in environment (1 paper), Meta-analysis and systematic reviews (1 paper) and COVID-19 Prevention and Impact (1 paper). The work is most often cited by research in Statistics and Probability (57 citations), General Psychology (5 citations), Statistics, Probability and Uncertainty (14 citations), General Decision Sciences (5 citations) and Astronomy and Astrophysics (30 citations). Yuling Yao has collaborated with scholars based in United States, Germany and Finland. Frequent co-authors include Andrew Gelman, Aki Vehtari, Daniel Simpson, Jonah Gabry, Måns Magnusson, Topi Paananen, Paul‐Christian Bürkner, Alexander van Geen, Tyler K. Ellis and Donald D. Lien. Their work appears in journals such as JAMA Network Open, Bayesian Analysis, Computational Brain & Behavior, Physical review. D and Journal of Physics G Nuclear and Particle Physics.
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.