Learning dynamical systems with particle stochastic approximation EM
This provides an incremental improvement for researchers in signal processing and related fields working with latent variable models in dynamical systems.
The authors tackled the problem of learning dynamical systems by developing the particle stochastic approximation EM (PSAEM) algorithm, which combines stochastic approximation EM with particle Gibbs with ancestor sampling to achieve superior computational performance and convergence properties compared to existing particle-smoothing-based EM approximations.
We present the particle stochastic approximation EM (PSAEM) algorithm for learning of dynamical systems. The method builds on the EM algorithm, an iterative procedure for maximum likelihood inference in latent variable models. By combining stochastic approximation EM and particle Gibbs with ancestor sampling (PGAS), PSAEM obtains superior computational performance and convergence properties compared to plain particle-smoothing-based approximations of the EM algorithm. PSAEM can be used for plain maximum likelihood inference as well as for empirical Bayes learning of hyperparameters. Specifically, the latter point means that existing PGAS implementations easily can be extended with PSAEM to estimate hyperparameters at almost no extra computational cost. We discuss the convergence properties of the algorithm, and demonstrate it on several signal processing applications.