Bayesian analysis of non-linear differential equation models with application to a gut microbial ecosystem

DJ Lawson, G Holtrop, H Flint

Research output: Contribution to journalArticle (Academic Journal)peer-review

3 Citations (Scopus)

Abstract

Process models specified by non-linear dynamic differential equations contain many parameters, which often must be inferred from a limited amount of data. We discuss a hierarchical Bayesian approach combining data from multiple related experiments in a meaningful way, which permits more powerful inference than treating each experiment as independent. The approach is illustrated with a simulation study and example data from experiments replicating the aspects of the human gut microbial ecosystem. A predictive model is obtained that contains prediction uncertainty caused by uncertainty in the parameters, and we extend the model to capture situations of interest that cannot easily be studied experimentally.
Translated title of the contributionBayesian analysis of non-linear differential equation models with application to a gut microbial ecosystem
Original languageEnglish
Pages (from-to)543 - 556
Number of pages14
JournalBiometrical Journal
Volume53, issue 4
DOIs
Publication statusPublished - Jul 2011

Bibliographical note

Publisher: Wiley-Blackwell

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