Robust maximum-likelihood estimation of multivariable dynamic systems

By Stuart Gibson, Brett Ninness. at - Automatisierungstechnik, 2005

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Least‐squares parameter estimation for systems with irregularly missing data (2009, methodology): “Identification problems of systems with missing samples have received much attentions in the statistical literature using the expectation maximization (EM) approach [18, 19].”
Learning deep dynamical models from image pixels (2014, unknown): “, subspace methods (Van Overschee and De Moor, 1996), expectation maximization (Shumway and Stoffer, 1982; Ghahramani, 1998; Gibson and Ninness, 2005) and prediction-error methods (Ljung, 1999).”
Identification of jump Markov linear models using particle filters (2014, methodology): “There are also many approaches considering the more general problem with an unknown number of modes K and an unknown state dimension nz, see e.g. [12] and [4], making use of Bayesian nonparametric models and mixed integer programming, respectively.”
System identification of nonlinear state-space models (2011, methodology): “Taking the conditional expectation (15a) in order to compute Q(θ, θ k ) is then simply achieved by invoking a modification of a standard Kalman smoother [16] [24].”