14th IFAC Symposium on System Identification, SYSID 2006

SYSID-2006 Paper Abstract


Paper WeB4.1

Bell, Stephen Thomas (Lincoln University, New Zealand), McKinnon, Alan E (Lincoln University, New Zealand), Sykes, Andrew R (Lincoln University, New Zealand)

Modelling Biological Variation: An Algorithm for Parameter Distribution Estimation.

Scheduled for presentation during the Invited Session "Biomedical Parameter Identification: Methods and Clinical Applications" (WeB4), Wednesday, March 29, 2006, 15:30−15:50, Cummings Room

14th IFAC Symposium on System Identification, March 29 - 31, 2006, Newcastle, Australia

This information is tentative and subject to change. Compiled on July 17, 2018

Keywords Biological Systems, Particle Filtering/Monte Carlo Methods, Multivariable System Identification


Abstract: Parameters in a dynamic model of magnesium metabolism in dairy cattle were implemented as distributions to represent biological variation between individual animals, thus allowing calculation of the proportion of animals in the herd at risk of developing hypomagnesaemic tetany by Monte-Carlo simulation. This paper describes an algorithm developed to refine a priori parameter distribution estimates, and discusses its principles of operation, performance, and convergence properties. When the estimated parameter distributions are used in the model they generate a response distribution of the model of the required accuracy. The algorithm is specified in general terms permitting application to other models.