| d-blink: Distributed end-to-end Bayesian entity resolution NG Marchant, A Kaplan, DN Elazar, BIP Rubinstein, RC Steorts Journal of Computational and Graphical Statistics 30 (2), 406-421, 2021 | 28 | 2021 |
| Designing modular software: a case study in introductory statistics E Hare, A Kaplan Journal of Computational and Graphical Statistics 26 (3), 493-500, 2017 | 11 | 2017 |
| Tracking the transmission dynamics of COVID‐19 with a time‐varying coefficient state‐space model JP Keller, T Zhou, A Kaplan, GB Anderson, W Zhou Statistics in Medicine 41 (15), 2745-2767, 2022 | 10 | 2022 |
| A practical approach to proper inference with linked data A Kaplan, B Betancourt, RC Steorts The American Statistician 76 (4), 384-393, 2022 | 9* | 2022 |
| On the S-instability and degeneracy of discrete deep learning models A Kaplan, DJ Nordman, SB Vardeman Information and Inference: A Journal of the IMA 9 (3), 627-655, 2020 | 7* | 2020 |
| Properties and Bayesian fitting of restricted Boltzmann machines A Kaplan, D Nordman, S Vardeman Statistical Analysis and Data Mining: The ASA Data Science Journal 12 (1), 23-38, 2019 | 5 | 2019 |
| Fast Bayesian Record Linkage for Streaming Data Contexts I Taylor, A Kaplan, B Betancourt Journal of Computational and Graphical Statistics, 1-12, 2024 | 4 | 2024 |
| Simulating Markov random fields with a conclique-based Gibbs sampler A Kaplan, MS Kaiser, SN Lahiri, DJ Nordman Journal of Computational and Graphical Statistics 29 (2), 286-296, 2020 | 4 | 2020 |
| Can you buy a president? Politics after the Tillman Act A Kaplan, E Hare, H Hofmann, D Cook Chance 27 (1), 20-30, 2014 | 4 | 2014 |
| An interactive graphical method for community detection in network data A Kaplan, H Hofmann, D Nordman Computational statistics 32, 535-557, 2017 | 3 | 2017 |
| Improving wildlife population inference using aerial imagery and entity resolution X Lu, MB Hooten, A Kaplan, JN Womble, MR Bower Journal of Agricultural, Biological and Environmental Statistics 27 (2), 364-381, 2022 | 2 | 2022 |
| Putting down roots: a graphical exploration of community attachment AJ Kaplan, ER Hare Computational Statistics 34 (4), 1449-1464, 2019 | 2 | 2019 |
| On advancing MCMC-based methods for Markovian data structures with applications to deep learning, simulation, and resampling A Kaplan Iowa State University, 2017 | 2 | 2017 |
| Statistical and Mathematical Self-Efficacy of Incoming Students at a Large Public University U Genschel, A Kaplan, A Carriquiry, E Johnston, W Kliemann, K Koehler, ... Sustainability in Statistics Education, Proceedings of the Ninth …, 2014 | 2 | 2014 |
| Interactive Exploration of Large Dendrograms with Prototypes A Kaplan, J Bien The American Statistician 77 (2), 201-211, 2023 | 1 | 2023 |
| A Bayesian Record Linkage Approach to Applications in Tree Demography Using Overlapping LiDAR Scans L Drew, A Kaplan, I Breckheimer arXiv preprint arXiv:2501.13285, 2025 | | 2025 |
| Generative Filtering for Recursive Bayesian Inference with Streaming Data I Taylor, A Kaplan, B Betancourt Journal of Computational and Graphical Statistics, 1-20, 2024 | | 2024 |
| A formal goodness-of-fit test for spatial binary Markov random field models E Biswas, A Kaplan, MS Kaiser, DJ Nordman Biometrics 80 (4), ujae119, 2024 | | 2024 |
| The Fast and the Furious: Tracking the Effect of the Tomoa Skip on Speed Climbing C Chou, A Kaplan arXiv preprint arXiv:2411.13696, 2024 | | 2024 |
| Data from" A Bayesian Record Linkage Approach to Applications in Tree Demography Using Overlapping LiDAR Scans" L Drew, A Kaplan, I Breckheimer Environmental System Science Data Infrastructure for a Virtual Ecosystem …, 2024 | | 2024 |