A comparison of approximate versus exact techniques for Bayesian parameter inference in nonlinear ordinary differential equation models.

ABSTRACT: The behaviour of many processes in science and engineering can be accurately described by dynamical system models consisting of a set of ordinary differential equations (ODEs). Often these models have several unknown parameters that are difficult to estimate from experimental data, in which case Bayesian inference can be a useful tool. In principle, exact Bayesian inference using Markov chain Monte Carlo (MCMC) techniques is possible; however, in practice, such methods may suffer from slow convergence and poor mixing. To address this problem, several approaches based on approximate Bayesian computation (ABC) have been introduced, including Markov chain Monte Carlo ABC (MCMC ABC) and sequential Monte Carlo ABC (SMC ABC). While the system of ODEs describes the underlying process that generat

SUBMITTER: Alahmadi AA 

PROVIDER: S-EPMC7137938 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature