Multi-component model engineering

摘要

Multi-component model engineering is described, for example, to model multi-component dynamical systems in which the true underlying processes are incompletely understood such as the Earth's biosphere, whole organisms, biological cells, the immune system, and anthropogenic systems such as agricultural systems, and economic systems. In an embodiment individual component models are linked together and associated with empirical data observed from the system being modeled in a consistent, repeatable manner. For example, a model component, its links with data, its outputs, and its links with other model components, are specified in a format to be passed directly to inference routines which use an inference engine to infer the most likely parameters of the multi-component model given subsets of the empirical data. The inferred parameter values take the form of a probability distribution representing the degree of uncertainty in most likely parameter. An embodiment describes ways of identifying model components for revising.

主权项

1. A multi-component model engineering system comprising:
one or more processors arranged to provide a library of model components each comprising a function for describing the behavior of a sub-set of the state variables of a multi-component dynamical system, and having at least one parameter, the multicomponent dynamical system including a multi-component model wherein each component of the multi-component model is a model comprising one or more functions representing biological or physical processes and their interactions; a data access engine arranged to access empirical data measured from the dynamical system, the data being accessed from a plurality of different datasets; an inference engine arranged to learn one or more of the parameters of specified ones of the model components and to learn a probability distribution for each parameter which represents a degree of uncertainty of the parameter; a model-data association engine arranged to link a plurality of specified model components from the library of model components to form a multi-component model and to associate each model component with parameters to be learnt, with data from at least one of the datasets; the model-data association engine also being arranged to pass the specified model components to the inference engine in a format suitable for use by the inference engine to learn the parameters and probability distributions of the specified model components using the data associated with the specified model components; and a model engineering system arranged to obtain a first learnt probability distribution based on a model fitting process for each of the model component of the entire multi-component model and a second learnt probability distribution based on a model fitting process of an individual model component, and automatically revise the individual model component to produce a revised model component if the difference between the first and second learnt probability distributions exceeds a threshold value, the revised model component retaining one or more elements of the individual model component.