Mathematical simulation models are commonly applied to analyze experimental or environmental data and eventually to acquire predictive capabilities. Typically these models depend on poorly defined, unmeasurable parameters that need to be given a value. Fitting a model to data, so-called inverse modelling, is often the sole way of finding reasonable values for these parameters. There are many challenges involved in inverse model applications, e. g., the existence of non-identifiable parameters, the estimation of parameter uncertainties and the quantification of the implications of these uncertainties on model predictions.
The R package F M E is a modeling package designed to confront a mathematical model with data. It includes algorithms for sensitivity and Monte Carlo analysis, parameter identifiability, model fitting and provides a Markov-chain based method to estimate parameter confidence intervals. Although its main focus is on mathematical systems that consist of differential equations, F M E can deal with other types of models. In this paper, F M E is applied to a model describing the dynamics of the HIV virus.
|Number of pages||28|
|Journal||Journal of statistical software|
|Publication status||Published - Feb 2010|
Research priority areas of TU Dresden
DFG Classification of Subject Areas according to Review Boards
Sustainable Development Goals
- simulation models, differential equations, fitting, sensitivity, Monte Carlo, identifiability, R, simulation models, differential equations, fitting, sensitivity, Monte Carlo, identifiability, R