Abstract:
:The simulation of biological systems is often plagued by a high level of noise in the data, as well as by models containing a large number of correlated parameters. As a result, the parameters are poorly identified by the data, and the reliability of the model predictions may be questionable. Bayesian sampling methods provide an avenue for proper statistical analysis in such situations. Nevertheless, simulations should employ models that, on the one hand, are reduced as much as possible, and, on the other hand, are still able to capture the essential features of the phenomena studied. Here, in the case of algae growth modeling, we show how a systematic model reduction can be done. The simplified model is analyzed from both theoretical and statistical points of view.
journal_name
Bull Math Bioljournal_title
Bulletin of mathematical biologyauthors
Haario H,Kalachev L,Laine Mdoi
10.1007/s11538-009-9417-7subject
Has Abstractpub_date
2009-10-01 00:00:00pages
1626-48issue
7eissn
0092-8240issn
1522-9602journal_volume
71pub_type
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