Bayesian system identification of dynamical systems using highly informative training data
This paper addresses the situation where, with the aim of conducting Bayesian system identification, one is presented with very large sets of training data. Techniques are developed which allow one to choose a subset of the available training data which is highly informative with regards to both levels of Bayesian inference  parameter estimation and model selection. Examples include the system identification of various systems using both physics-based and data-based models.
bayesian inference nonlinear system identification markov chain monte carlo shannon entropy tamar bridge