Learning sparse dynamic linear systems using stable spline kernels and exponential hyperpriors

Part of Advances in Neural Information Processing Systems 23 (NIPS 2010)

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Alessandro Chiuso, Gianluigi Pillonetto


We introduce a new Bayesian nonparametric approach to identification of sparse dynamic linear systems. The impulse responses are modeled as Gaussian processes whose autocovariances encode the BIBO stability constraint, as defined by the recently introduced “Stable Spline kernel”. Sparse solutions are obtained by placing exponential hyperpriors on the scale factors of such kernels. Numerical experiments regarding estimation of ARMAX models show that this technique provides a definite advantage over a group LAR algorithm and state-of-the-art parametric identification techniques based on prediction error minimization.