| NIMBLE: MCMC, particle filtering, and programmable hierarchical modeling P de Valpine, C Paciorek, D Turek, N Michaud, C Anderson-Bergman, ... R package version 0.11 1, 2021 | 146 | 2021 |
| NIMBLE user manual P de Valpine, C Paciorek, D Turek, N Michaud, C Anderson-Bergman, ... R package manual version 0.9 1, 2020 | 51* | 2020 |
| Centered partition processes: informative priors for clustering (with discussion) S Paganin, AH Herring, AF Olshan, DB Dunson, TNBDP Study Bayesian analysis 16 (1), 301, 2021 | 24 | 2021 |
| NIMBLE: MCMC, Particle Filtering, and Programmable Hierarchical Modeling. 2020 P de Valpine, C Paciorek, D Turek, N Michaud, C Anderson-Bergman, ... R package version 0.12 2, 2020 | 16 | 2020 |
| Bayesian modeling of networks in complex business intelligence problems D Durante, S Paganin, B Scarpa, DB Dunson Journal of the Royal Statistical Society: Series C (Applied Statistics) 66 …, 2015 | 13 | 2015 |
| Computational strategies and estimation performance with Bayesian semiparametric Item Response Theory models S Paganin, CJ Paciorek, C Wehrhahn, A Rodriguez, S Rabe-Hesketh, ... arXiv preprint arXiv:2101.11583, 2021 | 12* | 2021 |
| Bayesian nonparametric latent class analysis for different item types M Qiu, S Paganin, I Ohn, L Lin Multivariate Behavioral Research 58 (1), 156-157, 2023 | 5 | 2023 |
| Informed Random Partition Models with Temporal Dependence S Paganin, GL Page, FA Quintana arXiv preprint arXiv:2311.14502, 2023 | 1 | 2023 |
| Allergic Rhinitis and Asthma: Relationship with Transverse Maxillary Contraction and Transverse Expansion Stability in Children G Ottaviano, L Favero, S Hajrulla, A Volpato, S Paganin, G Bissolotti, ... Applied Sciences 13 (5), 3200, 2023 | 1 | 2023 |
| compareMCMCs: An R package for studying MCMC efficiency P de Valpine, S Paganin, D Turek Journal of Open Source Software 7 (69), 3844, 2022 | 1 | 2022 |
| Computational methods for fast Bayesian model assessment via calibrated posterior p-values S Paganin, P Valpine Journal of Computational and Graphical Statistics, 1-19, 2024 | | 2024 |
| New Frontiers in Bayesian Statistics: BAYSM 2021, Online, September 1–3 R Argiento, F Camerlenghi, S Paganin Springer Nature, 2022 | | 2022 |
| Package ‘compareMCMCs’ P de Valpine, S Paganin, D Turek, C Paciorek | | 2022 |
| Semiparametric IRT models for non-normal latent traits S Paganin CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS, 178, 2021 | | 2021 |
| Bayesian IRT models in NIMBLE. S Paganin, C Paciorek, P de Valpine Book of Short Papers SIS 2020, 644-649, 2020 | | 2020 |
| Domain knowledge based priors for clustering S Paganin Proceedings of the Conference of the Italian Statistical Society" Smart …, 2019 | | 2019 |
| Discussion on:" Latent nested nonparametric priors", Federico Camerlenghi, David B. Dunson, Antonio Lijoi, Igor Prünster, Abel Rodríguez, Bayesian Analysis 14, 4, 1303–1356 E Aliverti, S Paganin, T Rigon, M Russo BAYESIAN ANALYSIS, 2019 | | 2019 |
| Prior-driven cluster allocation in bayesian mixture models S Paganin University of Padova, 2018 | | 2018 |
| Hierarchical Graphical Model for Learning Functional Network Determinants E Aliverti, L Forastiere, T Padellini, S Paganin, E Wit Studies in Neural Data Science, 23-36, 2017 | | 2017 |
| Modeling of Complex Network Data for Targeted Marketing S Paganin SIS 2017 Statistics and Data Science: new challenges, new generations, 753, 2017 | | 2017 |
Perry de ValpineEnvironmental Science, Policy and Management, University of California, BerkeleyE-mail megerősítve itt: berkeley.edu
