Bayesian workflow A Gelman, A Vehtari, D Simpson, CC Margossian, B Carpenter, Y Yao, ...
arXiv preprint arXiv:2011.01808, 2020
325 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