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Bayesian variable selection in linear dynamical systems
Aalto, Atte; Goncalves, Jorge
2018
 

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Abstract :
[en] We develop a method for reconstructing regulatory interconnection networks between variables evolving according to a linear dynamical system. The work is motivated by the problem of gene regulatory network inference, that is, finding causal effects between genes from gene expression time series data. In biological applications, the typical problem is that the sampling frequency is low, and consequentially the system identification problem is ill-posed. The low sampling frequency also makes it impossible to estimate derivatives directly from the data. We take a Bayesian approach to the problem, as it offers a natural way to incorporate prior information to deal with the ill-posedness, through the introduction of sparsity promoting prior for the underlying dynamics matrix. It also provides a framework for modelling both the process and measurement noises. We develop Markov Chain Monte Carlo samplers for the discrete-valued zero-structure of the dynamics matrix, and for the continuous-time trajectory of the system.
Disciplines :
Mathematics
Author, co-author :
Aalto, Atte ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Goncalves, Jorge ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Language :
English
Title :
Bayesian variable selection in linear dynamical systems
Publication date :
15 February 2018
Number of pages :
19
Focus Area :
Systems Biomedicine
Available on ORBilu :
since 22 February 2018

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