| Bayesian workflow A Gelman, A Vehtari, D Simpson, CC Margossian, B Carpenter, Y Yao, ... arXiv preprint arXiv:2011.01808, 2020 | 324 | 2020 |
| A review of automatic differentiation and its efficient implementation CC Margossian Wiley interdisciplinary reviews: data mining and knowledge discovery 9 (4 …, 2019 | 310 | 2019 |
| Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: A modeling study in Hubei, China, and six regions in Europe A Hauser, MJ Counotte, CC Margossian, G Konstantinoudis, N Low, ... PLoS medicine 17 (7), e1003189, 2020 | 277* | 2020 |
| Stan modeling language users guide and reference manual Stan Development Team Technical report, 2016 | 272 | 2016 |
| Planet hunters. VII. Discovery of a new low-mass, low-density planet (PH3 C) orbiting Kepler-289 with mass measurements of two additional planets (PH3 B and D) JR Schmitt, E Agol, KM Deck, LA Rogers, JZ Gazak, DA Fischer, J Wang, ... The Astrophysical Journal 795 (2), 167, 2014 | 58 | 2014 |
| Bayesian workflow for disease transmission modeling in Stan L Grinsztajn, E Semenova, CC Margossian, J Riou Statistics in medicine 40 (27), 6209-6234, 2021 | 47 | 2021 |
| mrgsolve: simulate from ODE-based population PK/PD and systems pharmacology models KT Baron, A Hindmarsh, L Petzold, B Gillespie, C Margossian, D Pastoor R package version 0.8 6, 2017 | 39* | 2017 |
| Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond C Margossian, A Vehtari, D Simpson, R Agrawal Advances in Neural Information Processing Systems 33, 9086-9097, 2020 | 35 | 2020 |
| Nested : Assessing the convergence of Markov chain Monte Carlo when running many short chains CC Margossian, MD Hoffman, P Sountsov, L Riou-Durand, A Vehtari, ... arXiv preprint arXiv:2110.13017, 2021 | 12 | 2021 |
| The discrete adjoint method: Efficient derivatives for functions of discrete sequences M Betancourt, CC Margossian, V Leos-Barajas arXiv preprint arXiv:2002.00326, 2020 | 11 | 2020 |
| Differential equations based models in stan C Margossian, B Gillespie | 11 | 2017 |
| Flexible and efficient Bayesian pharmacometrics modeling using Stan and Torsten, Part I CC Margossian, Y Zhang, WR Gillespie CPT: Pharmacometrics & Systems Pharmacology 11 (9), 1151-1169, 2022 | 8 | 2022 |
| Stan functions for Bayesian pharmacometric modeling C Margossian, WR Gillespie J Pharmacokinet Pharmacodyn 43, S52, 2016 | 8 | 2016 |
| The shrinkage-delinkage trade-off: An analysis of factorized gaussian approximations for variational inference CC Margossian, LK Saul Uncertainty in Artificial Intelligence, 1358-1367, 2023 | 6 | 2023 |
| Gaining Efficiency by Combining Analytical and Numerical Solutions to Solve ODE Systems: Implementation in Stan and Application in Bayesian PKPD Modeling CC Margossian, WR Gillespie JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS 44, S61-S61, 2017 | 5* | 2017 |
| Adaptive tuning for Metropolis adjusted Langevin trajectories L Riou-Durand, P Sountsov, J Vogrinc, C Margossian, S Power International Conference on Artificial Intelligence and Statistics, 8102-8116, 2023 | 4 | 2023 |
| Approximate Bayesian inference for latent Gaussian models in Stan CC Margossian, A Vehtari, D Simpson, R Agrawal Stan Con 2020, 2020 | 4 | 2020 |
| Efficient automatic differentiation of implicit functions CC Margossian, M Betancourt arXiv preprint arXiv:2112.14217, 2021 | 3 | 2021 |
| Solving ODEs in a Bayesian context: challenges and opportunities CC Margossian, L Zhang, S Weber, A Gelman Population Approach Group in Europe (PAGE) 29, 2021 | 3 | 2021 |
| Computing steady states with stan’s nonlinear algebraic solver CC Margossian Stan Conference 2018 California, 2018 | 3 | 2018 |
