We use linear Gaussian state-space models to analyse time-course gene expression data of yeast. They are modelled to be generated from hidden state variables in a system. To identify the system, we estimate parameters of the model by EM algorithm and determine the dimension of the state variable by BIC.
['Rui Yamaguchi', 'Tomoyuki Higuchi']
State-space approach with the maximum likelihood principle to identify the system generating time-course gene expression data of yeast