Learning dynamical systems with particle stochastic approximation EM

Understand

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.