title: Technical note: Bayesian calibration of dynamic ruminant nutrition models.
abstract: Mechanistic models of ruminant digestion and metabolism have advanced our understanding of the processes underlying ruminant animal physiology. Deterministic modeling practices ignore the inherent variation within and among individual animals and thus have no way to assess how sources of error influence model outputs. We introduce Bayesian calibration of mathematical models to address the need for robust mechanistic modeling tools that can accommodate error analysis by remaining within the bounds of data-based parameter estimation. For the purpose of prediction, the Bayesian approach generates a posterior predictive distribution that represents the current estimate of the value of the response variable, taking into account both the uncertainty about the parameters and model residual variability. Predictions are expressed as probability distributions, thereby conveying significantly more information than point estimates in regard to uncertainty. Our study illustrates some of the technical advantages of Bayesian calibration and discusses the future perspectives in the context of animal nutrition modeling.
authors.affiliation: Department of Animal Science, University of California, Davis 95616.
authors.forename: K F
authors.initials: KF
authors.lastname: Reed
authors.affiliation: Department of Physical and Environmental Sciences, University of Toronto, Toronto, Ontario, MIC 1A4, Canada.
authors.forename: G B
authors.initials: GB
authors.lastname: Arhonditsis
authors.affiliation: Centre for Nutrition Modelling, Department of Animal Biosciences, University of Guelph, Guelph, Ontario N1G 2W1, Canada.
authors.forename: J
authors.initials: J
authors.lastname: France
authors.affiliation: Department of Animal Science, University of California, Davis 95616. Electronic address: ekebreab@ucdavis.edu.
authors.forename: E
authors.initials: E
authors.lastname: Kebreab
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D002138:Calibration; D008962:Models, Theoretical; D012418:Ruminants; D035501:Uncertainty
keywords: Bayesian methods; mechanistic modeling; ruminant