2018/06/25 by Lindholm, Andreas, Fredrik Lindsten, Lindsten, Fredrik
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Computation (stat.CO) #Computational Engineering #FOS: Computer and information sciences #FOS: Electrical engineering #Finance #Gaussian Processes and Bayesian Inference #Machine Learning (stat.ML) #Signal Processing (eess.SP) #Statistical Methods and Bayesian Inference #and Science (cs.CE) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1806.09548
openalex publication_date 2018/06/25 · openalex created_date 2018/07/10 · openalex updated_date 2026/07/28
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.