Bayesian Inference for High-Dimensional ODE Models with Time-Varying Parameters via Relaxation-Reference-Cited by-同舟云学术

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Bayesian Inference for High-Dimensional ODE Models with Time-Varying Parameters via Relaxation

Published:2023-12-16 Issue: Volume: Page:119-124
ISSN:
Container-title:2023 IEEE 11th Conference on Systems, Process & Control (ICSPC)
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Author:

Waseel Farhad¹,Streftaris George²,Rudrusamy Bhuvendhraa³,Dass Sarat C.⁴

Affiliation:

1. Heriot-Watt University Malaysia, Kabul University,Mathematical and Computer Sciences, Faculty of Mathematics,Putrajaya,Malaysia

2. Heriot-Watt University (of Maxwell Institute for Mathematical Sciences),Mathematical and Computer Sciences,Edinburgh,United Kingdom

3. School of Engineering and Physical Sciences Heriot-Watt University Malaysia,Putrajaya,Malaysia

4. School of Mathematical and Computer Sciences Heriot-Watt University Malaysia,Putrajaya,Malaysia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10419865/10419882/10420365.pdf?arnumber=10420365

Reference12 articles.

1. Inference for differential equation models using relaxation via dynamical systems

2. Bayesian computational methods for state-space models with application to SIR model

3. A new extension of state-space SIR model to account for Underreporting – An application to the COVID-19 transmission in California and Florida

4. Forecasting seasonal influenza with a state-space SIR model

5. A data driven change-point epidemic model for assessing the impact of large gathering and subsequent movement control order on COVID-19 spread in Malaysia

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