A0585
Title: Bayesian parameter inference and model selection for differential equation models
Authors: Shijia Wang - Nankai University (China) [presenting]
Abstract: Nonlinear ordinary differential equations (ODEs) are used in a wide range of scientific problems to model complex dynamic systems, for example, transmission models for COVID-19. The differential equations often contain unknown parameters that are of scientific interest, which have to be estimated from noisy measurements of the dynamic system. Generally, there is no closed-form solution for nonlinear ODEs, and the likelihood surface for the parameter of interest is multi-modal and very sensitive to different parameter values. We will introduce our proposed sequential Monte Carlo (SMC) to conduct Bayesian inference for parameters in ODEs and model selection.
