C. Margossian

发表

Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: a modelling study in Hubei, China and northern Italy

Anthony Hauser, Julien Riou, Nicola Low, 2020, medRxiv.

Bayesian workflow for disease transmission modeling in Stan

Charles C. Margossian, Elizaveta Semenova, L'eo Grinsztajn, 2020, Statistics in medicine.

Adaptive Tuning for Metropolis Adjusted Langevin Trajectories

Pavel Sountsov, Lionel Riou-Durand, Jure Vogrinc, 2022, 2210.12200.

Bayesian Workflow.

Aki Vehtari, Bob Carpenter, Jonah Gabry, 2020, 2011.01808.

Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: A modeling study in Hubei, China, and six regions in Europe

N. Low, C. Althaus, J. Riou, 2020, PLoS medicine.

Supplementary appendix to: Estimation of SARS-CoV-2 age-speciﬁc mortality during the early stages of an epidemic: a modelling study in Hubei, China and northern Italy

N. Low, C. Althaus, J. Riou, 2020 .

E STIMATION OF SARS-C O V-2 MORTALITY DURING THE EARLY STAGES OF AN EPIDEMIC : A MODELLING STUDY IN H UBEI , C HINA AND NORTHERN I TALY

N. Low, C. Althaus, J. Riou, 2020 .

Bayesian workflow for disease transmission modeling in Stan

J. Riou, C. Margossian, Elizaveta Semenova, 2020, Statistics in medicine.

Flexible and efficient Bayesian pharmacometrics modeling using Stan and Torsten, Part I

C. Margossian, W. Gillespie, Yi Zhang, 2021, CPT: pharmacometrics & systems pharmacology.

Nested $\hat R$: Assessing the convergence of Markov chain Monte Carlo when running many short chains

A. Gelman, M. Hoffman, Pavel Sountsov, 2021, 2110.13017.

A review of automatic differentiation and its efficient implementation

Charles C. Margossian, C. Margossian, 2018, WIREs Data Mining Knowl. Discov..

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)

C. Lintott, K. Schawinski, Arfon M. Smith, 2014, 1410.8114.

Nested ˆ R : Assessing Convergence for Markov chain Monte Carlo when using many short chains

Matthew D. Hoffman, Charles C. Margossian, Pavel Sountsov, 2021 .

Supplementary appendix to: Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: a modeling study in Hubei, China, and six regions in Europe

N. Low, C. Althaus, J. Riou, 2020 .

Stan-Dev/Stan V2.17.0

Aki Vehtari, Bob Carpenter, Martin Černý, 2017 .

The Shrinkage-Delinkage Trade-off: an Analysis of Factorized Gaussian Approximations for Variational Inference

L. Saul, C. Margossian, 2023, UAI.

Amortized Variational Inference: When and Why?

D. Blei, C. Margossian, 2023, ArXiv.

Efficient Automatic Differentiation of Implicit Functions

M. Betancourt, C. Margossian, 2021, 2112.14217.

Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond

Aki Vehtari, Daniel Simpson, Charles C. Margossian, 2020, NeurIPS.

Nested $\hat R$: Assessing the convergence of Markov chain Monte Carlo when running many short chains

A. Gelman, M. Hoffman, Pavel Sountsov, 2021, 2110.13017.

Hamiltonian Monte Carlo using an embedded Laplace approximation

Daniel P. Simpson, Raj Agrawal, A. Vehtari, 2020, 2004.12550.

Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: a modelling study in Hubei, China and northern Italy

Christian L. Althaus, Anthony Hauser, Julien Riou, 2020, medRxiv.

Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs

C. Margossian, Loucas Pillaud-Vivien, Lawrence K. Saul, 2024, 2403.13748.

For how many iterations should we run Markov chain Monte Carlo?

C. Margossian, Andrew Gelman, 2023, 2311.02726.