Peter investigated algorithms for fitting dynamic models to noisy observations by minimum least mean square error estimation. The chosen algorithm was tested by simulation before using real observations from the chamber experiment. FMC observations were simulated by adding Gaussian noise to the output of an FMC model proposed by Matthews et al. The fitted model can generate predictions that match the simulated observations (see the figure, right). Once experimental FMCs are measured, these observations will be fitted to the different models using this algorithm, which allows us to compare the validity of these models.