Optimized expected information gain for nonlinear dynamical systems

  title={Optimized expected information gain for nonlinear dynamical systems},
  author={Alberto Giovanni Busetto and Cheng Soon Ong and Joachim M. Buhmann},
This paper addresses the problem of active model selection for nonlinear dynamical systems. We propose a novel learning approach that selects the most informative subset of time-dependent variables for the purpose of Bayesian model inference. The model selection criterion maximizes the expected Kullback-Leibler divergence between the prior and the posterior probabilities over the models. The proposed strategy generalizes the standard D-optimal design, which is obtained from a uniform prior with… CONTINUE READING
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