On-line Bayesian system identification
On-line Bayesian system identification
We consider an on-line system identification setting, in which new data become available at given time steps. In order to meet real-time estimation requirements, we propose a tailored Bayesian system identification procedure, in which the hyper-parameters are still updated through Marginal Likelihood maximization, but after only one iteration of a suitable iterative optimization algorithm. Both gradient methods and the EM algorithm are considered for the Marginal Likelihood optimization. We compare this "1-step" procedure with the standard one, in which the optimization method is run until convergence to a local minimum. The experiments we perform confirm the effectiveness of the approach we propose.
- University of Padua Italy
FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, FOS: Electrical engineering, electronic engineering, information engineering, Applications (stat.AP), Machine Learning (stat.ML), Systems and Control (eess.SY), Electrical Engineering and Systems Science - Systems and Control, Statistics - Applications, Machine Learning (cs.LG)
FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, FOS: Electrical engineering, electronic engineering, information engineering, Applications (stat.AP), Machine Learning (stat.ML), Systems and Control (eess.SY), Electrical Engineering and Systems Science - Systems and Control, Statistics - Applications, Machine Learning (cs.LG)
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