Towards Improved Uncertainty Quantification of Stochastic Epidemic Models Using Sequential Monte Carlo-Reference-Cited by-同舟云学术

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Towards Improved Uncertainty Quantification of Stochastic Epidemic Models Using Sequential Monte Carlo

Published:2024-05-27 Issue: Volume: Page:843-852
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Container-title:2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
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Author:

Fadikar Arindam¹,Stevens Abby¹,Collier Nicholson¹,Toh Kok Ben²,Morozova Olga³,Hotton Anna⁴,Clark Jared⁵,Higdon David⁵,Ozik Jonathan¹

Affiliation:

1. Decision and Infrastructure Sciences, Argonne National Laboratory,Lemont,IL,USA

2. Northwestern University,Department of Preventive Medicine,Chicago,IL,USA

3. University of Chicago,Department of Public Health Sciences,Chicago,IL,USA

4. University of Chicago,Department of Medicine,Chicago,IL,USA

5. Virginia Tech,Department of Statistics,Blacksburg,VA,USA

Funder

National Science Foundation

U.S. Department of Energy

Argonne National Laboratory

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10596331/10596332/10596408.pdf?arnumber=10596408

Reference36 articles.

1. A Monte Carlo Implementation of the Nonlinear Filtering Problem to Produce Ensemble Assimilations and Forecasts

2. Bayesian History Matching of Complex Infectious Disease Models Using Emulation: A Tutorial and a Case Study on HIV in Uganda

3. Curse-of-dimensionality revisited: Collapse of the particle filter in very large scale systems

4. Model reduction and approximation: theory and algorithms;Benner,2017

5. An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo

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