Bayes linear analysis for ordinary differential equations

• Published in: Computational statistics & data analysis Vol. 161; p. 107228
• Main Authors: Jones, Matthew, Goldstein, Michael, Randell, David, Jonathan, Philip
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2021
• Subjects: air; Bayes linear analysis; Bayesian theory; data analysis; differential equation; Graphical models; gravity; Numerical discrepancy; Structural discrepancy; uncertainty; Uncertainty quantification; Uncertainty quantification; Numerical discrepancy; Structural discrepancy; Graphical models; Bayes linear analysis
• ISSN: 0167-9473
• DOI: 10.1016/j.csda.2021.107228

Differential equation models are used in a wide variety of scientific fields to describe the behaviour of physical systems. Commonly, solutions to given systems of differential equations are not available in closed-form; in such situations, the solution to the system is generally approximated numerically. The numerical solution obtained will be systematically different from the (unknown) true solution implicitly defined by the differential equations. Even if it were known, this true solution would be an imperfect representation of the behaviour of the real physical system that it was designed to represent. A Bayesian framework is proposed which handles all sources of numerical and structural uncertainty encountered when using ordinary differential equation (ODE) models to represent real-world processes. The model is represented graphically, and the graph proves to be useful tool, both for deriving a full prior belief specification and for inferring model components given observations of the real system. A general strategy for modelling the numerical discrepancy induced through choice of a particular solver is outlined, in which the variability of the numerical discrepancy is fixed to be proportional to the length of the solver time-step and a grid-refinement strategy is used to study its structure in detail. A Bayes linear adjustment procedure is presented, which uses a junction tree derived from the originally specified directed graphical model to propagate information efficiently between model components, lessening the computational demands associated with the inference. The proposed framework is illustrated through application to two examples: a model for the trajectory of an airborne projectile moving subject to gravity and air resistance, and a model for the coupled motion of a set of ringing bells and the tower which houses them.